{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "77gENRVX40S7"
      },
      "source": [
        "##### Copyright 2019 The TensorFlow Authors."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "cellView": "form",
        "colab": {},
        "colab_type": "code",
        "id": "d8jyt37T42Vf"
      },
      "outputs": [],
      "source": [
        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "cellView": "form",
        "colab": {},
        "colab_type": "code",
        "id": "aPxHdjwW5P2j"
      },
      "outputs": [],
      "source": [
        "#@title MIT License\n",
        "#\n",
        "# Copyright (c) 2017 François Chollet                                                                                                                    # IGNORE_COPYRIGHT: cleared by OSS licensing\n",
        "#\n",
        "# Permission is hereby granted, free of charge, to any person obtaining a\n",
        "# copy of this software and associated documentation files (the \"Software\"),\n",
        "# to deal in the Software without restriction, including without limitation\n",
        "# the rights to use, copy, modify, merge, publish, distribute, sublicense,\n",
        "# and/or sell copies of the Software, and to permit persons to whom the\n",
        "# Software is furnished to do so, subject to the following conditions:\n",
        "#\n",
        "# The above copyright notice and this permission notice shall be included in\n",
        "# all copies or substantial portions of the Software.\n",
        "#\n",
        "# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n",
        "# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n",
        "# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n",
        "# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n",
        "# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n",
        "# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n",
        "# DEALINGS IN THE SOFTWARE."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "hRTa3Ee15WsJ"
      },
      "source": [
        "# Transfer Learning Using Pretrained ConvNets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "dQHMcypT3vDT"
      },
      "source": [
        "\u003ctable class=\"tfo-notebook-buttons\" align=\"left\"\u003e\n",
        "  \u003ctd\u003e\n",
        "    \u003ca target=\"_blank\" href=\"https://www.tensorflow.org/alpha/tutorials/images/transfer_learning\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" /\u003eView on TensorFlow.org\u003c/a\u003e\n",
        "  \u003c/td\u003e\n",
        "  \u003ctd\u003e\n",
        "    \u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/images/transfer_learning.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /\u003eRun in Google Colab\u003c/a\u003e\n",
        "  \u003c/td\u003e\n",
        "  \u003ctd\u003e\n",
        "    \u003ca target=\"_blank\" href=\"https://github.com/tensorflow/docs/blob/master/site/en/tutorials/images/transfer_learning.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /\u003eView source on GitHub\u003c/a\u003e\n",
        "  \u003c/td\u003e\n",
        "\u003c/table\u003e"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "2X4KyhORdSeO"
      },
      "source": [
        "In this tutorial we will discuss how to classify cats vs dogs images by using transfer learning from a pre-trained network. This will allows us to get higher accuracies than we saw by training our network from scratch.\n",
        "\n",
        "A **pre-trained model **is a saved network that was previously trained on a large dataset, typically on a large-scale image-classification task. We can either use the pretrained model as it is or transfer learning using the pretrained convents. The intuition behind **transfer learning **is that if this model trained on a large and general enough dataset, this model will effectively serve as a generic model of the visual world. We can leverage these learned feature maps without having to train a large model on a large dataset by using these models as the basis of our own model specific to our task. There are 2 scenarios of transfer learning using a pretrained model:\n",
        "\n",
        "1. **Feature Extraction** - use the representations of learned by a previous network to extract meaningful features from new samples. We simply add a new classifier, which will be trained from scratch, on top of the pretrained model so that we can repurpose the feature maps learned previously for our dataset. **Do we use the entire pretrained model or just the convolutional base?** - We use the feature extraction portion of these pretrained convnets (convolutional base) since they are likely to be generic features and learned concepts over a picture. However, the classification part of the pretrained model is often specific to original classification task, and subsequently specific to the set of classes on which the model was trained. \n",
        "2. **Fine-Tuning** - unfreezing a few of the top layers of a frozen model base used for feature extraction, and jointly training both the newly added classifier layers as well as the last layers of the frozen model. This allows us to \"fine tune\" the higher order feature representations in addition to our final classifier in order to make them more relevant for the specific task involved. \n",
        "\n",
        "**We will follow the general machine learning workflow:**\n",
        "1. Examine and understand data\n",
        "2. Build an input pipeline - using Keras ImageDataGenerator as we did in the image classification tutorial\n",
        "3. Compose our model \n",
        "  * Load in our pretrained model (and pretrained weights)\n",
        "  * Stack our classification layers on top\n",
        "4. Train our model\n",
        "5. Evaluate model\n",
        "\n",
        "We will see an example of using the pre-trained convnet as the feature extraction and then fine-tune to train the last few layers of the base model.\n",
        "\n",
        "**Audience:** This post is geared towards beginners with some Keras API and ML background. To get the most out of this post, you should have some basic ML background, know what CNNs are, and be familiar with the Keras Sequential API.  \n",
        "\n",
        "**Time Estimated**: 30 minutes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "iBMcobPHdD8O"
      },
      "outputs": [],
      "source": [
        "from __future__ import absolute_import, division, print_function\n",
        "\n",
        "import os\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 50257,
          "status": "ok",
          "timestamp": 1551651554353
        },
        "id": "TqOt6Sv7AsMi",
        "outputId": "1e4dec69-8578-4edf-e824-5858003f2002"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting tensorflow-gpu==2.0.0-alpha0\n",
            "Successfully installed google-pasta-0.1.4 tb-nightly-1.14.0a20190303 tensorflow-estimator-2.0-preview-1.14.0.dev2019030300 tensorflow-gpu==2.0.0-alpha0-2.0.0.dev20190303\n"
          ]
        }
      ],
      "source": [
        "!pip install tensorflow-gpu==2.0.0-alpha0\n",
        "import tensorflow as tf\n",
        "\n",
        "keras = tf.keras"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "v77rlkCKW0IJ"
      },
      "source": [
        "## Data preprocessing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "0GoKGm1duzgk"
      },
      "source": [
        "### Data download"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "vHP9qMJxt2oz"
      },
      "source": [
        "Use [TensorFlow Datasets](http://tensorflow.org/datasets) to load the cats and dogs dataset.\n",
        "\n",
        "This `tfds` package is the easiest way to load pre-defined data. If you have your own data, and are interested in importing using it with TensroFlow see [loading image data](../load_data/images.ipynb)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "KVh7rDVAuW8Y"
      },
      "outputs": [],
      "source": [
        "import tensorflow_datasets as tfds"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Nsoic6bGuwQ-"
      },
      "source": [
        "The `tfds.load` method downloads and caches the data, and returns a `tf.data.Dataset` obejct. These obejcts provide powerful, efficient methods for manipulating data and piping it into your model.\n",
        "\n",
        "Since `\"cats_vs_dog\"` doesn't define standard splits, use the subsplit feature to divide it into (train, validation, test) with 80%, 10%, 10% of the data respectively. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1615,
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/colabwidgets/controls.css": {
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loader sees only one file at a time. This allows postcss to see the variable
definitions when they are used. */

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| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

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This file is copied from the JupyterLab project to define default styling for
when the widget styling is compiled down to eliminate CSS variables. We make one
change - we comment out the font import below.
*/

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 * The material design colors are adapted from google-material-color v1.2.6
 * https://github.com/danlevan/google-material-color
 * https://github.com/danlevan/google-material-color/blob/f67ca5f4028b2f1b34862f64b0ca67323f91b088/dist/palette.var.css
 *
 * The license for the material design color CSS variables is as follows (see
 * https://github.com/danlevan/google-material-color/blob/f67ca5f4028b2f1b34862f64b0ca67323f91b088/LICENSE)
 *
 * The MIT License (MIT)
 *
 * Copyright (c) 2014 Dan Le Van
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation files (the "Software"), to deal
 * in the Software without restriction, including without limitation the rights
 * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 * copies of the Software, and to permit persons to whom the Software is
 * furnished to do so, subject to the following conditions:
 *
 * The above copyright notice and this permission notice shall be included in
 * all copies or substantial portions of the Software.
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 * SOFTWARE.
 */

 /*
The following CSS variables define the main, public API for styling JupyterLab.
These variables should be used by all plugins wherever possible. In other
words, plugins should not define custom colors, sizes, etc unless absolutely
necessary. This enables users to change the visual theme of JupyterLab
by changing these variables.

Many variables appear in an ordered sequence (0,1,2,3). These sequences
are designed to work well together, so for example, `--jp-border-color1` should
be used with `--jp-layout-color1`. The numbers have the following meanings:

* 0: super-primary, reserved for special emphasis
* 1: primary, most important under normal situations
* 2: secondary, next most important under normal situations
* 3: tertiary, next most important under normal situations

Throughout JupyterLab, we are mostly following principles from Google's
Material Design when selecting colors. We are not, however, following
all of MD as it is not optimized for dense, information rich UIs.
*/

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 * Optional monospace font for input/output prompt.
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 /* Commented out in ipywidgets since we don't need it. */

 /* @import url('https://fonts.googleapis.com/css?family=Roboto+Mono'); */

 /*
 * Added for compabitility with output area
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 :root {

  /* Borders

  The following variables, specify the visual styling of borders in JupyterLab.
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  /* UI Fonts

  The UI font CSS variables are used for the typography all of the JupyterLab
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  */ /* Base font size */ /* Ensures px perfect FontAwesome icons */

  /* Use these font colors against the corresponding main layout colors.
     In a light theme, these go from dark to light.
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  /* Use these against the brand/accent/warn/error colors.
     These will typically go from light to darker, in both a dark and light theme
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  /* Content Fonts

  Content font variables are used for typography of user generated content.
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  /* Layout

  The following are the main layout colors use in JupyterLab. In a light
  theme these would go from light to dark.
  */

  /* Brand/accent */

  /* State colors (warn, error, success, info) */

  /* Cell specific styles */
  /* A custom blend of MD grey and blue 600
   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
  /* A custom blend of MD grey and orange 600
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  /* Notebook specific styles */

  /* Console specific styles */

  /* Toolbar specific styles */
}

 /* Copyright (c) Jupyter Development Team.
 * Distributed under the terms of the Modified BSD License.
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 /*
 * We assume that the CSS variables in
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 /* This file has code derived from PhosphorJS CSS files, as noted below. The license for this PhosphorJS code is:

Copyright (c) 2014-2017, PhosphorJS Contributors
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, this
  list of conditions and the following disclaimer.

* Redistributions in binary form must reproduce the above copyright notice,
  this list of conditions and the following disclaimer in the documentation
  and/or other materials provided with the distribution.

* Neither the name of the copyright holder nor the names of its
  contributors may be used to endorse or promote products derived from
  this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

*/

 /*
 * The following section is derived from https://github.com/phosphorjs/phosphor/blob/23b9d075ebc5b73ab148b6ebfc20af97f85714c4/packages/widgets/style/tabbar.css 
 * We've scoped the rules so that they are consistent with exactly our code.
 */

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}

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}

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    /* From Material Design Lite */
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    margin-left: 0;
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    /* Horizontal widgets */
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            box-sizing: border-box;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
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        -ms-flex-direction: row;
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    /* Vertical Widgets */
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
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        -ms-flex-direction: column;
            flex-direction: column;
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        -ms-flex-align: center;
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 .widget-box {
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    margin: 0;
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 .widget-gridbox {
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    display: grid;
    margin: 0;
    overflow: auto;
}

 .widget-hbox {
    -webkit-box-orient: horizontal;
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        -ms-flex-direction: row;
            flex-direction: row;
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 .widget-vbox {
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    padding-right: 10px;
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    display: inline-block;
    white-space: nowrap;
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    text-overflow: ellipsis;
    text-align: center;
    font-size: 13px;
    cursor: pointer;

    height: 28px;
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    -webkit-box-shadow: none;
            box-shadow: none;

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    color: rgba(255, 255, 255, 1);
    background-color: #1976D2;
}

 .jupyter-button.mod-primary:active {
    color: rgba(255, 255, 255, 1);
    background-color: #1976D2;
}

 /* Button "Success" Styling */

 .jupyter-button.mod-success {
    color: rgba(255, 255, 255, 1.0);
    background-color: #4CAF50;
}

 .jupyter-button.mod-success.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #388E3C;
 }

 .jupyter-button.mod-success:active {
    color: rgba(255, 255, 255, 1);
    background-color: #388E3C;
 }

 /* Button "Info" Styling */

 .jupyter-button.mod-info {
    color: rgba(255, 255, 255, 1.0);
    background-color: #00BCD4;
}

 .jupyter-button.mod-info.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #0097A7;
}

 .jupyter-button.mod-info:active {
    color: rgba(255, 255, 255, 1);
    background-color: #0097A7;
}

 /* Button "Warning" Styling */

 .jupyter-button.mod-warning {
    color: rgba(255, 255, 255, 1.0);
    background-color: #FF9800;
}

 .jupyter-button.mod-warning.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #F57C00;
}

 .jupyter-button.mod-warning:active {
    color: rgba(255, 255, 255, 1);
    background-color: #F57C00;
}

 /* Button "Danger" Styling */

 .jupyter-button.mod-danger {
    color: rgba(255, 255, 255, 1.0);
    background-color: #F44336;
}

 .jupyter-button.mod-danger.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #D32F2F;
}

 .jupyter-button.mod-danger:active {
    color: rgba(255, 255, 255, 1);
    background-color: #D32F2F;
}

 /* Widget Button*/

 .widget-button, .widget-toggle-button {
    width: 148px;
}

 /* Widget Label Styling */

 /* Override Bootstrap label css */

 .jupyter-widgets label {
    margin-bottom: 0;
    margin-bottom: initial;
}

 .widget-label-basic {
    /* Basic Label */
    color: black;
    font-size: 13px;
    overflow: hidden;
    text-overflow: ellipsis;
    white-space: nowrap;
    line-height: 28px;
}

 .widget-label {
    /* Label */
    color: black;
    font-size: 13px;
    overflow: hidden;
    text-overflow: ellipsis;
    white-space: nowrap;
    line-height: 28px;
}

 .widget-inline-hbox .widget-label {
    /* Horizontal Widget Label */
    color: black;
    text-align: right;
    margin-right: 8px;
    width: 80px;
    -ms-flex-negative: 0;
        flex-shrink: 0;
}

 .widget-inline-vbox .widget-label {
    /* Vertical Widget Label */
    color: black;
    text-align: center;
    line-height: 28px;
}

 /* Widget Readout Styling */

 .widget-readout {
    color: black;
    font-size: 13px;
    height: 28px;
    line-height: 28px;
    overflow: hidden;
    white-space: nowrap;
    text-align: center;
}

 .widget-readout.overflow {
    /* Overflowing Readout */

    /* From Material Design Lite
        shadow-key-umbra-opacity: 0.2;
        shadow-key-penumbra-opacity: 0.14;
        shadow-ambient-shadow-opacity: 0.12;
     */
    -webkit-box-shadow: 0 2px 2px 0 rgba(0, 0, 0, .2),
                        0 3px 1px -2px rgba(0, 0, 0, .14),
                        0 1px 5px 0 rgba(0, 0, 0, .12);

    box-shadow: 0 2px 2px 0 rgba(0, 0, 0, .2),
                0 3px 1px -2px rgba(0, 0, 0, .14),
                0 1px 5px 0 rgba(0, 0, 0, .12);
}

 .widget-inline-hbox .widget-readout {
    /* Horizontal Readout */
    text-align: center;
    max-width: 148px;
    min-width: 72px;
    margin-left: 4px;
}

 .widget-inline-vbox .widget-readout {
    /* Vertical Readout */
    margin-top: 4px;
    /* as wide as the widget */
    width: inherit;
}

 /* Widget Checkbox Styling */

 .widget-checkbox {
    width: 300px;
    height: 28px;
    line-height: 28px;
}

 .widget-checkbox input[type="checkbox"] {
    margin: 0px 8px 0px 0px;
    line-height: 28px;
    font-size: large;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 0;
        flex-shrink: 0;
    -ms-flex-item-align: center;
        align-self: center;
}

 /* Widget Valid Styling */

 .widget-valid {
    height: 28px;
    line-height: 28px;
    width: 148px;
    font-size: 13px;
}

 .widget-valid i:before {
    line-height: 28px;
    margin-right: 4px;
    margin-left: 4px;

    /* from the fa class in FontAwesome: https://github.com/FortAwesome/Font-Awesome/blob/49100c7c3a7b58d50baa71efef11af41a66b03d3/css/font-awesome.css#L14 */
    display: inline-block;
    font: normal normal normal 14px/1 FontAwesome;
    font-size: inherit;
    text-rendering: auto;
    -webkit-font-smoothing: antialiased;
    -moz-osx-font-smoothing: grayscale;
}

 .widget-valid.mod-valid i:before {
    content: "\f00c";
    color: green;
}

 .widget-valid.mod-invalid i:before {
    content: "\f00d";
    color: red;
}

 .widget-valid.mod-valid .widget-valid-readout {
    display: none;
}

 /* Widget Text and TextArea Stying */

 .widget-textarea, .widget-text {
    width: 300px;
}

 .widget-text input[type="text"], .widget-text input[type="number"]{
    height: 28px;
    line-height: 28px;
}

 .widget-text input[type="text"]:disabled, .widget-text input[type="number"]:disabled, .widget-textarea textarea:disabled {
    opacity: 0.6;
}

 .widget-text input[type="text"], .widget-text input[type="number"], .widget-textarea textarea {
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    border: 1px solid #9E9E9E;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    padding: 4px 8px;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    -ms-flex-negative: 1;
        flex-shrink: 1;
    outline: none !important;
}

 .widget-textarea textarea {
    height: inherit;
    width: inherit;
}

 .widget-text input:focus, .widget-textarea textarea:focus {
    border-color: #64B5F6;
}

 /* Widget Slider */

 .widget-slider .ui-slider {
    /* Slider Track */
    border: 1px solid #BDBDBD;
    background: #BDBDBD;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    position: relative;
    border-radius: 0px;
}

 .widget-slider .ui-slider .ui-slider-handle {
    /* Slider Handle */
    outline: none !important; /* focused slider handles are colored - see below */
    position: absolute;
    background-color: white;
    border: 1px solid #9E9E9E;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    z-index: 1;
    background-image: none; /* Override jquery-ui */
}

 /* Override jquery-ui */

 .widget-slider .ui-slider .ui-slider-handle:hover, .widget-slider .ui-slider .ui-slider-handle:focus {
    background-color: #2196F3;
    border: 1px solid #2196F3;
}

 .widget-slider .ui-slider .ui-slider-handle:active {
    background-color: #2196F3;
    border-color: #2196F3;
    z-index: 2;
    -webkit-transform: scale(1.2);
            transform: scale(1.2);
}

 .widget-slider  .ui-slider .ui-slider-range {
    /* Interval between the two specified value of a double slider */
    position: absolute;
    background: #2196F3;
    z-index: 0;
}

 /* Shapes of Slider Handles */

 .widget-hslider .ui-slider .ui-slider-handle {
    width: 16px;
    height: 16px;
    margin-top: -7px;
    margin-left: -7px;
    border-radius: 50%;
    top: 0;
}

 .widget-vslider .ui-slider .ui-slider-handle {
    width: 16px;
    height: 16px;
    margin-bottom: -7px;
    margin-left: -7px;
    border-radius: 50%;
    left: 0;
}

 .widget-hslider .ui-slider .ui-slider-range {
    height: 8px;
    margin-top: -3px;
}

 .widget-vslider .ui-slider .ui-slider-range {
    width: 8px;
    margin-left: -3px;
}

 /* Horizontal Slider */

 .widget-hslider {
    width: 300px;
    height: 28px;
    line-height: 28px;

    /* Override the align-items baseline. This way, the description and readout
    still seem to align their baseline properly, and we don't have to have
    align-self: stretch in the .slider-container. */
    -webkit-box-align: center;
        -ms-flex-align: center;
            align-items: center;
}

 .widgets-slider .slider-container {
    overflow: visible;
}

 .widget-hslider .slider-container {
    height: 28px;
    margin-left: 6px;
    margin-right: 6px;
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
}

 .widget-hslider .ui-slider {
    /* Inner, invisible slide div */
    height: 4px;
    margin-top: 12px;
    width: 100%;
}

 /* Vertical Slider */

 .widget-vbox .widget-label {
    height: 28px;
    line-height: 28px;
}

 .widget-vslider {
    /* Vertical Slider */
    height: 200px;
    width: 72px;
}

 .widget-vslider .slider-container {
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
    margin-left: auto;
    margin-right: auto;
    margin-bottom: 6px;
    margin-top: 6px;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
}

 .widget-vslider .ui-slider-vertical {
    /* Inner, invisible slide div */
    width: 4px;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    margin-left: auto;
    margin-right: auto;
}

 /* Widget Progress Styling */

 .progress-bar {
    -webkit-transition: none;
    transition: none;
}

 .progress-bar {
    height: 28px;
}

 .progress-bar {
    background-color: #2196F3;
}

 .progress-bar-success {
    background-color: #4CAF50;
}

 .progress-bar-info {
    background-color: #00BCD4;
}

 .progress-bar-warning {
    background-color: #FF9800;
}

 .progress-bar-danger {
    background-color: #F44336;
}

 .progress {
    background-color: #EEEEEE;
    border: none;
    -webkit-box-shadow: none;
            box-shadow: none;
}

 /* Horisontal Progress */

 .widget-hprogress {
    /* Progress Bar */
    height: 28px;
    line-height: 28px;
    width: 300px;
    -webkit-box-align: center;
        -ms-flex-align: center;
            align-items: center;

}

 .widget-hprogress .progress {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    margin-top: 4px;
    margin-bottom: 4px;
    -ms-flex-item-align: stretch;
        align-self: stretch;
    /* Override bootstrap style */
    height: auto;
    height: initial;
}

 /* Vertical Progress */

 .widget-vprogress {
    height: 200px;
    width: 72px;
}

 .widget-vprogress .progress {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    width: 20px;
    margin-left: auto;
    margin-right: auto;
    margin-bottom: 0;
}

 /* Select Widget Styling */

 .widget-dropdown {
    height: 28px;
    width: 300px;
    line-height: 28px;
}

 .widget-dropdown > select {
    padding-right: 20px;
    border: 1px solid #9E9E9E;
    border-radius: 0;
    height: inherit;
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    outline: none !important;
    -webkit-box-shadow: none;
            box-shadow: none;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    vertical-align: top;
    padding-left: 8px;
	appearance: none;
	-webkit-appearance: none;
	-moz-appearance: none;
    background-repeat: no-repeat;
	background-size: 20px;
	background-position: right center;
    background-image: url("data:image/svg+xml;base64,PD94bWwgdmVyc2lvbj0iMS4wIiBlbmNvZGluZz0idXRmLTgiPz4KPCEtLSBHZW5lcmF0b3I6IEFkb2JlIElsbHVzdHJhdG9yIDE5LjIuMSwgU1ZHIEV4cG9ydCBQbHVnLUluIC4gU1ZHIFZlcnNpb246IDYuMDAgQnVpbGQgMCkgIC0tPgo8c3ZnIHZlcnNpb249IjEuMSIgaWQ9IkxheWVyXzEiIHhtbG5zPSJodHRwOi8vd3d3LnczLm9yZy8yMDAwL3N2ZyIgeG1sbnM6eGxpbms9Imh0dHA6Ly93d3cudzMub3JnLzE5OTkveGxpbmsiIHg9IjBweCIgeT0iMHB4IgoJIHZpZXdCb3g9IjAgMCAxOCAxOCIgc3R5bGU9ImVuYWJsZS1iYWNrZ3JvdW5kOm5ldyAwIDAgMTggMTg7IiB4bWw6c3BhY2U9InByZXNlcnZlIj4KPHN0eWxlIHR5cGU9InRleHQvY3NzIj4KCS5zdDB7ZmlsbDpub25lO30KPC9zdHlsZT4KPHBhdGggZD0iTTUuMiw1LjlMOSw5LjdsMy44LTMuOGwxLjIsMS4ybC00LjksNWwtNC45LTVMNS4yLDUuOXoiLz4KPHBhdGggY2xhc3M9InN0MCIgZD0iTTAtMC42aDE4djE4SDBWLTAuNnoiLz4KPC9zdmc+Cg");
}

 .widget-dropdown > select:focus {
    border-color: #64B5F6;
}

 .widget-dropdown > select:disabled {
    opacity: 0.6;
}

 /* To disable the dotted border in Firefox around select controls.
   See http://stackoverflow.com/a/18853002 */

 .widget-dropdown > select:-moz-focusring {
    color: transparent;
    text-shadow: 0 0 0 #000;
}

 /* Select and SelectMultiple */

 .widget-select {
    width: 300px;
    line-height: 28px;

    /* Because Firefox defines the baseline of a select as the bottom of the
    control, we align the entire control to the top and add padding to the
    select to get an approximate first line baseline alignment. */
    -webkit-box-align: start;
        -ms-flex-align: start;
            align-items: flex-start;
}

 .widget-select > select {
    border: 1px solid #9E9E9E;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
    outline: none !important;
    overflow: auto;
    height: inherit;

    /* Because Firefox defines the baseline of a select as the bottom of the
    control, we align the entire control to the top and add padding to the
    select to get an approximate first line baseline alignment. */
    padding-top: 5px;
}

 .widget-select > select:focus {
    border-color: #64B5F6;
}

 .wiget-select > select > option {
    padding-left: 4px;
    line-height: 28px;
    /* line-height doesn't work on some browsers for select options */
    padding-top: calc(28px - var(--jp-widgets-font-size) / 2);
    padding-bottom: calc(28px - var(--jp-widgets-font-size) / 2);
}

 /* Toggle Buttons Styling */

 .widget-toggle-buttons {
    line-height: 28px;
}

 .widget-toggle-buttons .widget-toggle-button {
    margin-left: 2px;
    margin-right: 2px;
}

 .widget-toggle-buttons .jupyter-button:disabled {
    opacity: 0.6;
}

 /* Radio Buttons Styling */

 .widget-radio {
    width: 300px;
    line-height: 28px;
}

 .widget-radio-box {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    margin-bottom: 8px;
}

 .widget-radio-box label {
    height: 20px;
    line-height: 20px;
    font-size: 13px;
}

 .widget-radio-box input {
    height: 20px;
    line-height: 20px;
    margin: 0 8px 0 1px;
    float: left;
}

 /* Color Picker Styling */

 .widget-colorpicker {
    width: 300px;
    height: 28px;
    line-height: 28px;
}

 .widget-colorpicker > .widget-colorpicker-input {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 1;
        flex-shrink: 1;
    min-width: 72px;
}

 .widget-colorpicker input[type="color"] {
    width: 28px;
    height: 28px;
    padding: 0 2px; /* make the color square actually square on Chrome on OS X */
    background: white;
    color: rgba(0, 0, 0, .8);
    border: 1px solid #9E9E9E;
    border-left: none;
    -webkit-box-flex: 0;
        -ms-flex-positive: 0;
            flex-grow: 0;
    -ms-flex-negative: 0;
        flex-shrink: 0;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    -ms-flex-item-align: stretch;
        align-self: stretch;
    outline: none !important;
}

 .widget-colorpicker.concise input[type="color"] {
    border-left: 1px solid #9E9E9E;
}

 .widget-colorpicker input[type="color"]:focus, .widget-colorpicker input[type="text"]:focus {
    border-color: #64B5F6;
}

 .widget-colorpicker input[type="text"] {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    outline: none !important;
    height: 28px;
    line-height: 28px;
    background: white;
    color: rgba(0, 0, 0, .8);
    border: 1px solid #9E9E9E;
    font-size: 13px;
    padding: 4px 8px;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    -ms-flex-negative: 1;
        flex-shrink: 1;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
}

 .widget-colorpicker input[type="text"]:disabled {
    opacity: 0.6;
}

 /* Date Picker Styling */

 .widget-datepicker {
    width: 300px;
    height: 28px;
    line-height: 28px;
}

 .widget-datepicker input[type="date"] {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 1;
        flex-shrink: 1;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    outline: none !important;
    height: 28px;
    border: 1px solid #9E9E9E;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    padding: 4px 8px;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
}

 .widget-datepicker input[type="date"]:focus {
    border-color: #64B5F6;
}

 .widget-datepicker input[type="date"]:invalid {
    border-color: #FF9800;
}

 .widget-datepicker input[type="date"]:disabled {
    opacity: 0.6;
}

 /* Play Widget */

 .widget-play {
    width: 148px;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
}

 .widget-play .jupyter-button {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    height: auto;
}

 .widget-play .jupyter-button:disabled {
    opacity: 0.6;
}

 /* Tab Widget */

 .jupyter-widgets.widget-tab {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
}

 .jupyter-widgets.widget-tab > .p-TabBar {
    /* Necessary so that a tab can be shifted down to overlay the border of the box below. */
    overflow-x: visible;
    overflow-y: visible;
}

 .jupyter-widgets.widget-tab > .p-TabBar > .p-TabBar-content {
    /* Make sure that the tab grows from bottom up */
    -webkit-box-align: end;
        -ms-flex-align: end;
            align-items: flex-end;
    min-width: 0;
    min-height: 0;
}

 .jupyter-widgets.widget-tab > .widget-tab-contents {
    width: 100%;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    margin: 0;
    background: white;
    color: rgba(0, 0, 0, .8);
    border: 1px solid #9E9E9E;
    padding: 15px;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    overflow: auto;
}

 .jupyter-widgets.widget-tab > .p-TabBar {
    font: 13px Helvetica, Arial, sans-serif;
    min-height: 25px;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab {
    -webkit-box-flex: 0;
        -ms-flex: 0 1 144px;
            flex: 0 1 144px;
    min-width: 35px;
    min-height: 25px;
    line-height: 24px;
    margin-left: -1px;
    padding: 0px 10px;
    background: #EEEEEE;
    color: rgba(0, 0, 0, .5);
    border: 1px solid #9E9E9E;
    border-bottom: none;
    position: relative;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab.p-mod-current {
    color: rgba(0, 0, 0, 1.0);
    /* We want the background to match the tab content background */
    background: white;
    min-height: 26px;
    -webkit-transform: translateY(1px);
            transform: translateY(1px);
    overflow: visible;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab.p-mod-current:before {
    position: absolute;
    top: -1px;
    left: -1px;
    content: '';
    height: 2px;
    width: calc(100% + 2px);
    background: #2196F3;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab:first-child {
    margin-left: 0;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab:hover:not(.p-mod-current) {
    background: white;
    color: rgba(0, 0, 0, .8);
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-mod-closable > .p-TabBar-tabCloseIcon {
    margin-left: 4px;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-mod-closable > .p-TabBar-tabCloseIcon:before {
    font-family: FontAwesome;
    content: '\f00d'; /* close */
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabIcon,
.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabLabel,
.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabCloseIcon {
    line-height: 24px;
}

 /* Accordion Widget */

 .p-Collapse {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
}

 .p-Collapse-header {
    padding: 4px;
    cursor: pointer;
    color: rgba(0, 0, 0, .5);
    background-color: #EEEEEE;
    border: 1px solid #9E9E9E;
    padding: 10px 15px;
    font-weight: bold;
}

 .p-Collapse-header:hover {
    background-color: white;
    color: rgba(0, 0, 0, .8);
}

 .p-Collapse-open > .p-Collapse-header {
    background-color: white;
    color: rgba(0, 0, 0, 1.0);
    cursor: default;
    border-bottom: none;
}

 .p-Collapse .p-Collapse-header::before {
    content: '\f0da\00A0';  /* caret-right, non-breaking space */
    display: inline-block;
    font: normal normal normal 14px/1 FontAwesome;
    font-size: inherit;
    text-rendering: auto;
    -webkit-font-smoothing: antialiased;
    -moz-osx-font-smoothing: grayscale;
}

 .p-Collapse-open > .p-Collapse-header::before {
    content: '\f0d7\00A0'; /* caret-down, non-breaking space */
}

 .p-Collapse-contents {
    padding: 15px;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    border-left: 1px solid #9E9E9E;
    border-right: 1px solid #9E9E9E;
    border-bottom: 1px solid #9E9E9E;
    overflow: auto;
}

 .p-Accordion {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
}

 .p-Accordion .p-Collapse {
    margin-bottom: 0;
}

 .p-Accordion .p-Collapse + .p-Collapse {
    margin-top: 4px;
}

 /* HTML widget */

 .widget-html, .widget-htmlmath {
    font-size: 13px;
}

 .widget-html > .widget-html-content, .widget-htmlmath > .widget-html-content {
    /* Fill out the area in the HTML widget */
    -ms-flex-item-align: stretch;
        align-self: stretch;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 1;
        flex-shrink: 1;
    /* Makes sure the baseline is still aligned with other elements */
    line-height: 28px;
    /* Make it possible to have absolutely-positioned elements in the html */
    position: relative;
}

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 */",
              "headers": [
                [
                  "content-type",
                  "text/css"
                ]
              ],
              "ok": true,
              "status": 200,
              "status_text": ""
            }
          }
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 125940,
          "status": "ok",
          "timestamp": 1551651630041
        },
        "id": "ro4oYaEmxe4r",
        "outputId": "e5002392-64da-46db-f6ce-9c8458e107ba"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1mDownloading / extracting dataset cats_vs_dogs (786.68 MiB) to /root/tensorflow_datasets/cats_vs_dogs/2.0.0...\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "SPLIT_WEIGHTS = (8, 1, 1)\n",
        "splits = tfds.Split.TRAIN.subsplit(weighted=SPLIT_WEIGHTS)\n",
        "\n",
        "(raw_train, raw_validation, raw_test), metadata = tfds.load(\n",
        "    'cats_vs_dogs', split=list(splits),\n",
        "    with_info=True, as_supervised=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "o29EfE-p0g5X"
      },
      "source": [
        "The resulting `tf.data.Dataset` objects contain `(image, label)` pairs. Where the images have variable shape and 3 channels, and the label is a scalar."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 125926,
          "status": "ok",
          "timestamp": 1551651630043
        },
        "id": "GIys1_zY1S9b",
        "outputId": "fbe859a1-b53b-4d77-f164-312710436855"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u003c_OptionsDataset shapes: ((None, None, 3), ()), types: (tf.uint8, tf.int64)\u003e\n",
            "\u003c_OptionsDataset shapes: ((None, None, 3), ()), types: (tf.uint8, tf.int64)\u003e\n"
          ]
        }
      ],
      "source": [
        "print(raw_train)\n",
        "print(raw_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "yO1Q2JaW5sIy"
      },
      "source": [
        "Show the first two images and labels from the training set:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 707
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 127778,
          "status": "ok",
          "timestamp": 1551651631912
        },
        "id": "K5BeQyKThC_Y",
        "outputId": "a332cd19-d4d1-42bb-d5b9-ca8410102208"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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hBJS2q/7WgAopOzWVXhdhSELsi+O5Kpd3jkmRYsQWBRqFM4pWqVzmNniIEWNs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\nYYQVglEwjzDCCsH/AOuM+OFPduLgAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "\u003cFigure size 576x396 with 1 Axes\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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CAiEpKsNoXLAy6FM3jvGkIopzur0us+mEog70V46ztbXJiTvuwc3HrK4sI4IlqC5rJ44T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nlLDvYig4GE3p9x22traI45jpdMpKr81ytEuSH3ZbZrMZuozphB4ik5hK7rEOkjrT6NKOBoSQ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\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27UDdcz+F1uJbT3AO5QVnVpeSB0i/toU+pDuxEVorDuWBui4Fv6b79BetefL259g1jdi0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\neOMNUXldX0uaQd8L9LjpaVpRb7397vsCxTWGp08+YDEpSPOENDHYRhE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Xl2w2G8bjMVop\nTp49Yj7KaDZL6HuKcUZbl0RKsVkvGY/HTMcFRVFIuGmAdO/v7rLZbKg2K2zXoKyjyFKMVtTVhjQx\naCWigsViThQnJFlOUoyYLTTrVUVVlZycnHD//n1WK7EHFMHykqYi95/NZlxdXZFmBVlWkGUZt/Zz\nxnnG7nwmisNYUtCNMcRG8ejxu3zpS18iTgy/93v/N2iDNgl121OGzXy1qsQb2Fmur1dcXV1tzfna\nezlFo3nv/UdMp3Pu3bvDnTv32N3dZbm84vp6xTvvvs9oPGW5XPNv/uBr3D46Yjabk4/GvPXWW1gv\nAPY0h6YUlWgTQnid0nStx4d0dAgLFy1gWa5Lfvf3/hX/y//8M5SbIJBRism4IM+zYKPxZEEVOZlM\nODs7o9rIImuMIR2NKMsNNsy74jjFO0dZVVJhzRcYJXPYxXzK5eU1aZ7TNB3ffeuRqEnzUYgNS0nz\nlPOra0bTGUlecPLkMeOxtL6brsNFGXEcb032L734EZlFGs3+/j5Hh7dQ2jOfz5mMxuSJpm9vWoiS\n0O7Chh3TVrW07JqGsqqYhkLAJArtNFfhUJUkMp+PdESWJwGo0XO9OqcPCTM6CEVcABlIkegwaUKW\njzBZT1VVJJEhSmI5qJsE53u63tNaWSu1SQT32HuW65I4iTBRgoki6s7iek+Wpaw3JXgvWgGToD2i\nth/Ym7bfGsi1jnCIIlNHYoYfLAjeh1zUoN/I8zxAOEBrFcguftuhMibezmEVojr1QRFvTPLDtq0/\n9vqhm96QgA3w27/923zhC18giiJ+67d+i3/0j/4Ru7u7/K2/9bc4OztjsVhs/95iseD09PSHvoDO\nNjgiUcRpORGlcSynnlDxxbGRwMPI4117o/QxkKYjssXuh77mDQw1/KDHOln4QWFMLunCYwJUKwAA\nIABJREFUkaGzntlY1IN9LwPbVVWipiMWO3MWswlN02xJIlcXFwE4bGiqmrZuyLKEPBHMz3w+ZfbK\nDvfv3+eVV17h/r2H29c0nGKTJBXeZ0cAV4uM1/YeZ0Xo0jYWrT1ZNt6qMquyoY+kLx8NqY4qpree\nvJhuT0ACsEbaDzgcYnRXyqMjUaBqDWmkheDixLibFwYizXQy3w6vjYkpRhmT0Yyjg9t8o+9ZXp+z\nd7BDpDsePXlM5CztqqGzVlqaThMnmnVpubqWlIb5zpx7D8XbNB7lpGlCXBiur695/71HLJdLXvrY\nq2gHH/3Ea1R1y9OTUy7WNbfvvUgcp1xuOk7OLzk/Oeb8/Jw8jjGRp9qs2Z9NSY0l0obNuubi7JiH\nD+6wt7PHxfUVeZ5TFAXPnj3j5OyCTXjg2kYWk4O9Q/q2ZjYekeeK0VhmOFkS38i6nZdImesGvVkz\nnu0ynuRMpgV7uzu88847QofXhrJpmOzsoZRAFZxXXF6XvP766zgHh4e3+PxP/QVe+djHOT8/587R\nHvPpLETWVGRJwkdeeECSJDy8f49PfOIT/M7/9X9yfX3N2fklJhZIepJM2d8veOON73B5scY5DSrh\neimK46PJgnGWsLu7y3g0YTQu6DvP2eklXd/StQ7rFF3n0SpmNJoxnswoAz1nsbtLnue889a7XF0t\ncVrsQ816OHxFxEnKeDyRtpS1IXRXEG+tg2rT8PXXv8mPffoT1HWNAuqmIytyitGUJBXrz/J6zbMT\nyS10XjOZzfBOcb1aUm5qej9EzWju3bvH7mJO3/ecn55xeHhEXmRiJ6ob2rYh1ZqX7t9neV2x3myw\nDpJ0RNla0qwgSXPGkxmTYkSeJRzs7bHY24O44N69OxKYnKRMRkICmU+FXpOlMRFipm+qElqw3U2V\nOJnMthDwtm1xTgzcsyxlPJ9uEXjFTPISJ7ujoHZsBccWIr36viVKIkYjQcdJJS2bQjKZAy4E9Iqt\no0cq09l8FBLPHXkuIAJtIoo8xyQxTdNwvdyQZKnYEYrJNsjaeojx+EiU8n0w+Vun6FsBSyutUU7m\n4oIR03hvsV6i0VSiiHRKFCfoKMUrOaRbZG2QFm4TBF2JtDm1oY96QeeBrElaSQoNHhUg/q770UQs\nAMr/B9aK/+Jf/Av+/t//+/zDf/gP+fa3v818PufVV1/lH/yDf8CzZ8/4zGc+w7e+9S3+xt/4GwD8\n3b/7d7l9+zY///M//yO/yD+9/vT60+tPrz+9/vT6/+P6DxKy/O7v/i6//uu/zm/8xm8wmUz4/Oc/\nv/29L33pS/ztv/23+ct/+S9vlXIAJycnfPrTn/6hX/v/+K1fE5xYZIIZVUDRSvmAznJ42wcUmNAh\n4njwtNy054YAyuejL6Q/7LAhvw5CFaTFUhDHMW1nsdbfqCE7y6qusXi6WnxLQ8SO7XqWyyXeypBV\nEXH76IiDgwNeeUWSpNtWwKmDn00GuzI7yvOQXdZ1Ej+ktHy/z0mJoyimqqptjIoLMzIJyYxpVb/t\ne3/yz/04b37t9fBO6ud64WZr7rS+374fwPbrDm3jqhK14EC+AIizOJxUe7pKUGQKcL1lef2MR4/e\n4+LiGYf7M6aTjD988w3e+/7bYiswOV3v2VQNb37/bW7dusWtW4fcvXublz5yj6KQKCLbtfjBi6nk\ndbz76CleaXbme6zWJdOdBXXVhrbXlG9+6w956623WC2vSNOYrl4xGefMJ2MWOxPK1QXPnjwmjSN2\ndxc8fHAHG3B1VdNxHVI5Hj16wtvvvMcbHzh+5St/hYvzU8rVmo+++AJZHrPYLXC+Y726ZlxIVE21\nWdPaXhiIcUqWj8jG05D+7Tg9ESP95eUlTdeS5iOu1iUmypjO5zgfUYz2efT4KZtNyUdefJmPv/ox\n9hf7aDxFnosXshHvZJJnOCzL9YrxbMqjZ8cUoxGPnz7h2bNjnj294OmTZzx+fMzyes3Z2QVDOrxU\nGDXznSlJYmit3P+TyWSLQSuKAmsty+WS09NT8QTO5xRFxv7BnDSNKcuSyUREHI/f/4C33nqH8/Nz\n2rrDWseTxyccHizE9yVBITivcPjQhoNIOapyzcFU8d//t1+grzfMJyPKcs3t27cZj8ehZRlvMWTr\n9ZoPPvhgK1fvui5kCNZbUdatW7ck088r1us1m/WaqhSv5HQ65cmTJzw7OWV375Cr0uK8ZryzTzHZ\n4eRyw607d0Wo1rQspiNio5mMxkxmU0YzaT9aa1HOk6cZWnnKsmQ6HrGYiD9zMF7v7u7K8+Y1vZVK\nfcDCpSHOR0URGmi6jqbp+PGf+gt855vfwDsnkT+up2872r5jMhmhPLS9YBOH59JaRFhiW4ySSIYI\nJOMwkfEKWuaduF7WOJN+yEz+fAdsCPC11oaxR/DYIv5MwRs2eOuC4tmRmHg7V7PW0jUVWrltALiO\nsrC2yRiFSFqR/RCGHfJFh0Df520TSunteoS/yVr0XuFtz2wyklmmc/w3X/7CD91b/n3XD930VqsV\nv/qrv8o//sf/eCtK+aVf+iX++l//69y7d4+vfvWrvPzyy3zqU5/ib/7Nvymy9yji61//+rbq++Ou\n0WgkQgt9E0/ft41ktKHCGyulrsQQ3bD5hje+aZqb/u8Pqnu0loSC8KFrrSToDtnoEqD1Lcp6LB56\nYeUlkSHKFHku866+7eiU5vatW4xGIyaTCeNiwv7+PqPRiIvzK5IkYTLZCa0K6ekncSZt06anKjfb\nB1krQ902gYTi2Wb+Pff9DQpAIECcb26252/iwXdzU7TfMO6e/3PwYWD2h3BGQVkKQB2+lhX+XdeJ\nXcR7BzqhKCbUTUnZWu4u9vnJn/rz4BTvvvsu67LDq4iud6RZwZ279zk8OuDo7m3G8xlJqsXroz3l\neoV3ctA4Pj6md/DwhYdcX61J8gLnNYu9PbRJ+ODd97i4uKAqBV9ldIKODdPRmMXOjCTSfP/RY959\n7/vcv3OHO3duoyKN7xXZKKMNcujxeMrOoiV7egzA9fVS0gWygoODA+JEU3XX6MjTW0vV1IOvZitC\nUmgULggf5D2aBUp/nuc0XcvJ6TOSXJSdAy1fvE4Z08kOR0dHEiJqIvIsRXtomwrb94zzAhUpvIqF\nzxgZptMpq/WaOE5Y7OwxyhfcuX2fV1+RWdqTJ0/FuN91NE3De++9S9NULNdrettuN8SiKLaf/fX1\ntczPqkqid+qa8/NTNuUVk8mIu3fvEscxT58+pSwlCmb4+iqIJAbByfA8ea1BK6KwKeRZTJ4ZbHvN\n40dPGGURRSqJJUUxZjKZ04e5ufcaa3uiKCHPx2RZSp4XKAXWeTaltOKViug7T12JrSdNc5qq5fr6\nREz44YB5sLdPVTfszvfpvMJkEkm22N/j3t0H9E4UgeM0xfU12iPm9PBepWkqmLymZrMpKbKUcRGy\nLL3QiDxwcX61zTEE0OHfz/OcizAO0SYiUprURGgjm1jvJBHBYyV2zClUlFDWllhHNL3DdR5PMI9H\nyZZoQqAfKa1JshEm1tu4IGjxXv6Nru23whxRsndb6f/gCRQ4v2w68l7b7boAN0Zx6wabg/rQOhSZ\nSLzHTjYqhwYnRnRvrYhaBKyMs07U3dYSRTfaBIEWxFur1fObnrUe511Qef4nYG/+83/+z7m8vOSX\nf/mXt7/2Mz/zM/zyL//ydk7yd/7O3yHLMn7lV36Fr3zlKyil+MVf/MWtqOWPfQFpQhxmVs73DAGx\ncYjM0d5Rm1CZGLX15XXW4p3FBiVZEhnBBtmwYEeaCIVXng4nH6iW05EKUuKua9DaMMry0APvUUYz\nUjFoRefgYL6QEMvJjRjFtrLRRlHEJCtoy54kGqOcplz12+rO2ojNdUUxHhHphLZu0EkhicZNS6Qy\nrOiS5O84j1cRkU5xViTIaZpivVAIqqahdv0W3AxslWEDtWT4sY1Z8nK6EiVVMH0qg9Ue2wn4t3ON\nyKldT6Q0yzDvGsz1NlS2xhjSZMRi7xZxnmH7huWmx9PxV/6H/5F/+k//Kb/7r74KOqHtej7xmU/x\nmc9+loODA+Y7U5yvAI/tW6zSjIhFFh4l7N66z/nliqrtmewe0nQ9V9cbzpen/NH3vs/F6RmRbUi0\nxCqVVxe8/NID7hwdYq2Ajt979AFJXvDKJ17j1q0DTo6f0LctVd1zuVyidMLl5SVpmvLgwUMAvv61\nf8tLL73IgxcfcrW6wtMzmeY0bct6XeOKjNhomVs0XRAcJKRxgjHQND2u7TEkjOZTMXlrxeHeLsuy\noVwtqcqGYjTn2fFjRsWUh/df4P69e5gIijzh6uqSB/fukcayOFXVhqvjc+YLufciI0DryWjCYrFP\n3znee+8Rk5HicF9IIR9/5dWtQldmvsGriaWuSy4vL2Wz6FuurpYBRtCy2VSsVteBW9vTtiV1X7Fe\nl7z//iMRyfSO85NTjEm4ulrin+uMRCZhOk8ZFSlKi1qvs46ul9O7iYyAojcxb7/1Pp/+5CvM5rtk\nacx0NsfEMdY55jtTmkY21GfPnolC0wp5Zzwe8+zZM4piyqYscSHA+fz8entf7uzsMF2K3/L6Wogl\n0+mM8QS+/q3vsLN/xOc++nH2ju4RFwvqtkdFGuUhsi1RHm8PdjoxjIuRrG9ZTp6mIiSJE66vr8mC\nmbosSwGqq4jOOfpQpbetzMxUqIiKIsP3YmNywacmj1eE1onM512HiiPiJKLc1FRth7Vi5M9Go+fg\n7RVJIvQVpTxlWUtEVNtSV/0WgjAcTrb8Ya2JYkvUdfQ2oMQC6L/pBT9GXeGcYzKeYVH0vYiMotAd\nEq+qpOJILBF4JXnvYgMRIWAcGVyIAULFqPB+qfCarG3xOhLVd5iZa62xzm8tC85ZgRN4t/15Wa4x\nOtpCsP+k1w/d9H7+53/+/3Uu99M//dP/zq99+ctf5stf/vL/pxeQRAkq0FaUk/TdYjyRwaiTBphJ\ns3CCcfS+I05ilI/ouy7Q+yV81CskUzjQ531oMUQmQXkVWiNKkg0IVY8cZvA6AiN+krFLMEqjM7M1\nR2ploJHNI/YGYyT2o7qqcV48da53IhceCAPOM84z6lqIGHEsmKK2qajqltFoRN+IT8okBq0VTSO5\nXc47XO9p+x6lNSaKiZMUa5ttFQhQB55l398krA+tXedc+DlhQ5XTnA5VZZrFXF5ekmQxRsfUlXD2\nri6uQyvWcXV1RVEUZEWO8w3LdSNS89hwdnbK7Tu3iNOCjYVPff6n+LffeZNvfvs7OOf48T//kxzd\nPgwmY41RRRDzpBT5jHq9oetdANI6fAwoS0tCQ8/p9RmPHj3i0ZNTNus1+5nl9mLK8nKN1w2TBLyt\nWW82PDs5w8UZ0719Frduc7la0rSWsnI8OTsmjiJwDUU+YVTkXJ+eA/DJV1/i1tEhO/MROnI4pzg5\nPpMWYD4BAdWD15SbiiLL0dqxWm0YOUgTQ2Jimqbn4uljtPIc7s3QJqbvz7E9dK4nS2OyKObpo6f8\nuc/9FPPZAkVN3bcU0ynrpqNskPd6sssin0p70GhUFLG7s0vXQ1U1ONdwsLMbYqTkNLxarcRO03d4\nH+F9RFOWKBNhFNy7fSSqR29pmi5k6MlGZ4yMCC4vr7m6umB3f4/DW7dYLpcYY3jy5Blvfe/7287D\nTRUAv/iLv4RzPWkRSWu+admsG/I8Zzoe4/qOtim5fPYup6ePKZeXHJ9csjPLGI0Kjo6OaJua1fIa\nH1Lv0zQmzRMBprcVe4V4Uet6SdtU7C32efT+Bzx8+JD33vuABw8eYHTMvXsP+MY3vsH3v/cODx48\noMhHmFhsTsZEHB8fc3T3I/SdIwtQ+75pmUwm7O/NSUxM19TEcXTTTXE9Vxfr0P5doLXm6lrsTKOx\nZD3WQXnY93I4nEymRFrEeAPUAAuxMZg43nZvXN+hlMNrgYY511NtKmKTkCb5tpoacgx725FlCWVZ\nUlUb0lRGPpuNiJYkf+6GRpUkiRyWw7jEY+l6CYIVu0RIjzBuK5azXc96vd52mxJz0xHq+568EJSb\n61Wo1uIAr9a4oHJGD2noLVlu8H2H16GV2XtMnNBZR1XeqHmHHxJVNFSXEl0kvj+LUyLL+49uWfiP\nfjmF86Fk58Z8KApE6S7LL/hgWYgk4ddLrH2kFQp1s9EgJb/48sIHF+fiolFimlREsgH6TvKpogjt\nNdpI9MdIZRLzQSQ5f07gqaDonjN2eh/aimgif2O7oA/lPJqqr8KfkcpytRqqMU+pwmlKgfUSsdT3\nQkCIIkWkjbDq8GKmdRZjbggFwPYmf75N8KHZppUqdwhnlEXc4nvP+fKCqqroz/vtjK9tZV7QKbF4\nZFlClhuiyEtCQYCDJ0lCkqVcXi0xqaF1nnwy4wtf+u8YzXa4uLjg1q1bwRMkbEGhT4iK1imoG0vT\n9RLQm4yITETf15gooVxV1K1lMltwT8ccHx8z8tfge9pmQ+QtJvIEvhubzQaHwSlN3VqpODrL9999\nRNV03D68hXZWIkq6njQAp/cWO0zHGTpyWN/RW8tsNpPPZ73ckiuUd8RJRNMNrTiZg2oPRBqtFWkS\nYxIhUtR1xWw65tEHJ3idUq03RJHh6PAWBwcHFFnGpqmJ44w4TsiLmXg74xSiiDiKCWc2UE5mKy5C\neUUap6Q7KcvlkqurK5bXwph0Nvi+2pCMjUO5kLZhS2wslSrOkcUGo0a4zG3nuQJVn6C1wfWeOEpI\n4oQH9+6zmIunctj0qkoW2gcP7tF2NcRgW8sYxdGtDKy0QpNxhFYz9mYZk3HB08fvY/tafJxdJzPQ\nphFvKkJlcc6xmO9wdnGO0RGXl5dYKxi1xMTb1mPTNFLhhBDc2AigQjyG4ombJAU7OzvExogKe7Mk\nKVI2yxWj6YTd3V2yOICPmwbvJd/tebrR0Dm5uLi4aet1GkJ2pYkSjFFh/hRgDMG4rbUo0+XgLN61\ngUJy40d0241K65iu+3DEknOC86vXqw8B7IdoM8njvKl+bmZkiiGxIIqkIpNxCDjXh3tLWpsMHafw\nupVSwSeoww8VqsubFuOwMUnLU/4960EFRqtSsF4vkazQiO65eKAo3Nw367281slkcqOh2HaYNEYb\nmqpChTT1H+X6L2DTswIwdfJhaKUCkcVtgacohwr/817RBz+QVC1K0hKiG++GUtHNB6ckFPb5TQ+v\npSo0OlBf5IZEg9IK7U14DaHtagdfiqINpzmGr+M9IG1W23t671DhxkHf3Hwgpvuqam6EK5Fw6nQU\nxow4AThrEHOqDmnD/dYsmiQ39AS4Od11nQ898IE5Gvr1vf3Q6W9o4zpnieOIKCq2LL8Bej0LXjEf\nWiVZntBbS1mtKUuRP8dxzHw+5+rqCtt4Nsen7B7s8tJHX8ErOVWPp3PKuqWznibQJ4YH0ihNnBao\nqMcqCT4dbnwxHkOeZcymU3bnc5T36HWPbSU+apwlN6dTKzO5cTEhNilN3ZGErEXvPRcXVyxmc4rA\nftxsNtvFY3d3l7yQCClrZUblOktZykLqrcxBtIZ5LA+kjg16YBoqUF4WjoEusy4l2HYy35eEaIaE\njZTxaEJkFJvNBmUi2fDykTAfW4uJ5PuSUFFpT3e9LO6xEd6oUorxeAx4Nps1q5XM1OAm3bpthZ2Y\nF5II0rYt1vXS5dCyIQ3+5a5v2ZRrOZR4z97ejNVmiXU9m7IUD1uqtguS1prNRmZGo3GCrix7B/sh\nTFhmO7YPZmPbERuN6nbJjJz6Ly7POTt9gveK1bphPp8Th2xGpTx1VZGmCc724N0WEJGmMU0WoyJw\nWJ4eP+Hy+oLxdMRoUpCkKUmW0HQNaE9nOzrbs7OzQ+cFq9V1HfMiY7F7ICK4OMFEFtvXJIGW5GyH\nQg6Ave+RWzZwVuOYKDJ8qKti2QpBnHNbjcHA9b2ZocmGORzQsyzb2hoGj9/wGQ7IsOFA2/c9k+Af\nHji56/V6O58ffmxZneF5GpJdZP2R508sYAHi7xTOd0AU1l/wSOtSOYUzMmZxjiBsqWXjDOnlQ1dJ\nABA3XSbFUG1GEN4v7eSAoE3Eer2RZJbwnj1/7z6vQ9hu4N5vn1n3X3ulZ127LZ/lxnDBd3Ljx9DP\n4We6rtn63dI0D9lyH1ZtijJo+DVNrExooXq8DYNYPIlKcd7Td718+IhidFOuQptUh80znCy8vunH\ng7RgbVBGqQhlBPhMKMGt7yHS2GC2j6KIbHRjXEW1mDicdrS0PIwx6KgPm2yPR2ZYOtEQme2sxPth\nQ/Vi3Nx+zwP6LJANtNzEfWixDBQIoSlA3dSMxvl2g1jszjFKE0VKjMd9RbsssV4UWrPZDIejrmt2\nd3fZWcxYbSrWdc3F5Zq2bZnO9hhPFnS9lfiYxqF1t10YZEPVVG2LcxFV3dL1DVGaMUrlpH94f5+X\nHjyka2tW15dQlzy+/kACSJOcvb0FbdVSlR299+wu9ik7z2QyYbPckEwKsnTMp177HL17HWMykiSG\nUDkcHu4BMN2Z03UNCvGptc0G52BdbqSq8Y46zEYur6TdlwV2aN/bbSWbZRkHh4eihEtSZjs7XK9r\n9vf3uVxK6Oh4lNN2DSfPnrJ/eIujO3eETqMitI4DlisskM7RWTmcWOuIjRA04vgmHWI+nzKZjOi6\njvfee4/1er2t2obFrgntovG4+JBoSebZsnDnecpsNgmVeIdJPIt0zHQqqe27u7ucnp5+aBGdTGQB\nevDgSOY0jaPvLXEaM53PsdZQliXzeUhQp+Hw6B6f+bEfx0Sa5fkzLs/O+OY3v8lmU4UoqRXeO77z\nxuuMAqbv7OQZi/kM2/d0yhDFhnW54smzp7z55mNu357Ic6YUvetYbZY4PHfu3eH6+pr1ZsNkZ5/l\npmNnZ4fxuGA+n5JnAt3uqg2zaUFb11xvVljbkcbZVuUcxyl5Lv99fb0EVPDSds9VUTHrdRkOR5rp\ndLrduIy5EZUNValJ5MBwcXG1FRcN7+3g75PNTtp70g51uDa6QSoG8L3WEZvNRlifgWjTtt12wxye\ntyiK0E5eSxyb7cY4AEAGjUCke5qgkFRKEVkhLwHEAW9oTIQJ92PTdGgXMwRt217WwwF0LRgyOci2\nnaXrLL6RsVUEz63TN8pSodJk/47qdLPZyJhiENz9Ca//7Jve8E0OC7ZzImj5wetG0WiI4yiU9wFI\nG1qL8gb5LX7He1GAKmfkNO6E4el6edNF4iFKSyz0ogqRCg6hnHsnN4Z8bfcDNJoQz+E9cZoR6bCg\ne0vvLNpL1InzgViAQ0cGFGgPtu8p+1XY9MIm22l0Z25OTJHe3rhaR1ilEfuo3b5/NjyASstGhpfW\nhLfDaakPiqtBHizv9SA0Go1yjElommrbstMRaKXROgliGE+axnjvaDt5eIuiwCPS50UxJq9DRFPb\n0zQVeC/V0/a03IbKNA6f9VBxRIxnc2bzHcqyDJU3xFoRZTG+q9FKTslxmFUkJqi6nMWisbYhjTP2\nFvtESpBLoCQvbW+f2WSKq6UCywqZJQEhgV5atvWmwdqQDGGtBGYGYUiapmSJvBed7WnXEjU0m+6Q\npAI7cA5a2xPFkkRQNhaPoWgtbaeItaLzAn0sspzJZL6tbEWt9rzdRjY7FwQDg6R/IAUNz8RwHRwc\nyCYXYmOKQozP7eVFyFgbbyuTvu+5ffs2IIcsMVRPtkisbJwG64QNBueUrpsyny8CMktL8jpSKXd1\nQ2Iy+s4FYVJK53WoXoM1RWmiKCGKU7SGuw9eZlTMOD1f8v7772Jth1YJTbdhPJ7Tt2vG4zHLa0kb\nqcsKZxJUHGO7jizLiGI4OjpiZ3cXF/ihVdtsRV1N3zGZzzBpQtrrrU2j2qyIw6IcJRHr1QobSCVp\nOkEzPG8377NS0npL0xQTukrDZrLZVB/KhRsOnJJvmG0VpcOhQ4Xg1DRNtxCIoTM1iNSGz2qwFkRR\nRBoHA/lz/87wbzxf7Q1tQeDf+bkiCiMVHUSDcmCWrkJIsEn0lu07bN7yOmviaBDQ3VihtgG4sH3d\nQ+tSAl+HcZPFGKk4h47Z8xXzULUOG/9gpRjueQkCF6zZj3L9Z9/0dDhpyJsuIaU/uI9HsnXJ7E5p\n8jQPLYNI2tDcwFK9H3rUcg3MNgZxRz/4RTy9Eyacd3Kyds7hrEVp9aFTENxUkEI2uZHxmtC2iOI4\nKJBEzOLDjE5rTaSlJRNFCq09UaSJ4wSlDFUp7U5toptFyd20OnzAsfkBwOo1Wnfbw07fNfSdE5GM\nJqQhR8iON0iCBxqN4ya3ylPXJVqDtV34nqCuSxJjcJ3MxdI0BeUlBDQ8PHVdExmJFVqtSorxlNYr\nkixnamK6VlqhdV3T1JKcbntp0chmKlzB2WROkjicl3mIDmYArRVNWaFjLUDwtsF3rSjQMOHBdfjE\ng1dYJw+Z7T2xSak218QKIp2iVctisYfru+1icfv2bR4+fAgQKqOUru9pewfacHV5Tm8tSZYxyvMQ\nCRMTqZsYrL5tWa2usR60NhTFmLIeFnhPU8tpu0hk811tOmzvKIqM8UTo/Emchc/Q3cyHh9b30Plw\nHjmkhFN7gGFLp0BJZJbrmM7Govwry+1C6L3HJPL3hoVxuJ+H2dnQhpODkfy8a3uZjTUVo2JMnBgO\nDzNs6DIoNEkcTv8mwRtHFknwaNc7VlVD58PhJJMK0vcS16WRanK1LolMzsHhXY5PzmmbNXkhleXt\nozuslmckYc5YB7uEB5JENtf9wyP2z845unOPrBiJYtRZkixnd/+ApusZT6aYWEQhDrtNExnsGlpr\nvHW0zYaiyBkXY2mruhtI8yChlzinRIDLYeFHR/TOY+KULA/AZcDEN7l9ShuctyhtSNJYSFDhM8jz\nfNu6fH4Te95OFMc3SeJ1ud62PYWgcjPbcvht9yeK5dDsw33UNi0uWEocnrbpQodBEIjWdoGaIt0F\n4WuGkY4SEZxAwj1KWWzntjYeuU+jbRdMG40i6CCcRF15hWAcg3Ws73vSYhQKBitFjxbuAAAgAElE\nQVQUl6AWtTZ0IgYbC26oYYhTobf8Vy9k0RppOxFUcj+wqWwrnuEUFCWijApg6uFUYIwJVZnank6s\nFZ9I18MQ4WGt3b75Q3Dj4GvzXjLvTCwiGK0NkQ5y23Cjpmn8odc1nELWVY31UuGYJKEY58SxwSsX\nrBZKhsDPRYjgFLPZRAbLQN/L7CUfjambCmcFGNz3XRCAENBlfttyaKo14Ehjg7eWtm+4CWXU4j18\nbtOTilDaFFGk0JEmCmGcSsV4esZFLotpSIB3tkNpiQap6zqkNMtpN8symZEVM0Z5TtM0xJFlMhaA\n8Gq12j60A7/0eT+lC4u9NjGJiVB5xnq5Yn9vgcGyub5kVmTc2V9w9iin3FyjTSxkdzTrpiXOx1yt\nSp48PePicsn+zg7tbMRsMqJ3mqa2rJcrri9OmIxSPvrKx5jMZ/J+WkdZ1TRNw2ZTU1Y1eweH1HXJ\nwcEBWVaQxokkv683N5xBE6PKGnRCFOcU4zldyCN0riQfjzAhnQOkmj4/r7h/dMT+7lzCaTEheWGE\n1vDmm9+h6zpeffVVTk6fhfmLCBaqqsK5flsVDMZipSSENIkTYpMyGc/CcxWen2H8/KGOiiymfd9v\nT9SCt4owJhJDs4MiTvG9p+3kIGlMQuSkhTZKAz+SnLQoiOlkrGAiZqOINojFbPBWWSUbjPMeYzK8\n9swWuzyMx1xebzg7eSRKy2ND31Z89GMvc3Vxgk4ytIlI84zWR6yqjiQpWC/PeOUTr9FYhVMJVd1Q\nbUrSYsbe/pRiOmW1WdNYOLr7kMXeAXkxI01GjOcLqlLa7XFkQImCOjHyeVh/c+z23od1he1za31o\nB2pN17a0nUALBpW5U8/N2ACLoumlLalCVBhIGG8UGcbjYtt2ft52pLX8O+JFrrAOoshgPVvO6TCW\n0GH+L+vhzSG2qipms5kg02ppm+ajyYfWsN5ZbGf5f6h7kx9LsuzM73cHG97kc2RE5FRzFlkk1SKo\nJohGayFBgnZaSIJWWurv00oNqDeCpIaAVqsFiM0iiyxWFasyMjIGH5+/waY7aHHuNTOPLElg1SIl\nCwTcw8PfZGb3nnO+833fEb2e3LOmKFOiL9V7YawI5Us7wuMhONbrE6ypyMOrq6oiKOi6gb7rKKwM\nndVaowsJ5oUVg2qtdTLktmnSA4koJftsVRXJ97iRKo9pvNDvc3zrQS9nOjn4KDVBA1pZYmJPomQc\niIvC6BRcW0G0aEVqnOcSXo8XLAaN6+dwQK4EVYLcIkpJ41UpjTWWPKk9Qw1z3Bl4kpV1XUc79Axe\nPkNVFRR1KeaxRiDTGCFGeb8hirsB0WCNJc91y7P+JChlFmbAezdbBEJ8KArI4tO6lqxsGGYEmRlc\nLKzNHLjtSGgZM9GE51uboZEBH4YE0cpAXyE4hGlxZcf0kUxjcL4HLa9ptcFYqUiW9UKqXGWx2uDC\nAEGJIXjqhzgv3qCZDXZ5dY4momPk4vyU6FfcvH1NuahZb2q+/NUvaPpA52rOLq7YNx3VYilTxO8f\nCanvdLJec3Z2iYuKt2/f0nQtl+cCUWU/2dwD672jrBbo5PxTluWUcRvN6empfGbnOR6PHI9H6mqF\nVobt4w7ng8DFUaNSf6NtW0hU7cEHFIHDcc/t7TXVYk3dDjjrRghafDvjyKALYRo9I/2hEp3ujRz0\n8vHbNgKpNGQDzySBiVQVxmuX/8pj1DgFQH5XofV0/+d1Md1rBhAyh1JxhGPFc8GgdH5MchRJ1fZm\nucQH2Jyc8JM/+mP++1/9nNW65oc/+kPefP0l9w/3DF6zO3Q8f/4scZgLmuORZ59/wl/99Oes12s+\n/fRTtg8irl+vzjBGNHtXV1ccGklcPvnsO6yWp2gjlYItl2glelcRmBtk4JAi+IgykyQIYEj7h1Rd\ndlwHxhQUhRp/FnwkpsRSK0P2jixsKeOmlBn7+/n65GT8/+6Y0CtFWdaIPi6O92eGTOfvd7rW09ig\njCJl2cncocrakqJIFRsB51tIZ3w20DxdQ/lBWdREPH3n6PEjcaVNzNOYXKjmGt/o4xPYMr/XaeD3\nFMTnpDdJIMD5qcf3+xzfetBTUROcMDezu4XQypTEsFyoKEBpwaND0rZoDVFkDM47MoNNggwygNTJ\nrLQnTKBEFsiEF6302Ig1Jkkico9tDHry3EJlTuxRrfB4YhJs6jRksigKjFX4nOFoRhxfRRFy2nSz\nzmGEnIGH8XF6TAryEfFjfw/AFjLjLOLTacublLD/uhn9OQeVXE3LXxjpyiRMPkFdcs4lSKvErjXG\njlZQ+WZWRuGDkmkEBGKIyTHjgFVCRSd6onfEKCOfpAIuMQqUlaa8906cLpSmKA2F0uA6XILrdoc9\nzy4v6L2nsAVN21KuPG+v39N7Q+s8XT+wGBwe6F3gzVdf44LoMT//7DucbYSter99kM9gCkxRYQdP\nUchE8/1xS1nWnF9cyQYRZA6h0oagI+0wsN3vWVZLusGx3e1FP6egaY4UhYyD6bqOoqrlqg29TOTo\nO+5ur7k4f87i84XMQksQ9ouXH1EUloeHe5rmiHP9BGum4CybhECy3yQATOtqJDIxWc6N91AUTVle\nCzm5y8/je7Gwyk+otEarqc+jlJ6mX/rE9iOLlmUNjz1nWbYyJjoALhB9pGkCNzc3VFXB2cmaarHk\n6zdf8+bNG6rasKpWmKpmu2uplg0XVx/R7TtevPycw2Fgvb7g7OyMqtrwwx9+wQ9/+ENxUCkqVith\nw7ZtK4OKl2u0KhKELAnyarEURCQI1J779rIWTepnS3AfyOfXoLUd++JEkRWR0Aq0kNu0Es2iFnUI\nWltsascI6S2X3xJkfRopRFRoJXPnSImGQku1lcgdT4YZp6RM63kgCFgbx++Loh4DS05U27Yfrc1k\nL9TTa6mkYw6RmIynx6fXMPSt3I9VAUQOj3uIGmM0UYu0SymFMjIpoeu6lPinQIYf0a6cdGdW6xza\nzb8/RyiWM9j19zm+9aAn2UC2wkn6J1MAoisBJfY8Y2VHqtAmamsIkRimTMfnst1JIz6TXPKNa/Ik\nX12k6kyGtObMqU0CcHnuKSAAY0VlC6m6jBWnkxACprTJF1Qyd+cdVVWk6k2NG4JSMmJnaPop+KQb\n0xhD790Y9PqU5cuNq2c3gXx2uflJTffcg0yBVomuLA24EulDhOgNSkeMlqxKqNxCfhEhbao+U59g\nfo/l9zrPUK0uJIjJMiEoyfbD0BOMl8oOEpFIrqqOoKIjOJ8SDEtQ0n903qE6JZVy3xC8k4nVlzIQ\nt6wqBt+jizX94FmtT+gfm7HacwF6H9g+Hvjxj3/CarMm+p6r8yVvvn4FmtkGnivdkuBbAtLTqOsq\nkVyyn6pIPdq2pW16vIu0yqFCpG07FqsNbdOz2+2p64rzc7EkQxv6TkZTffT8RRrh1NB1SVtWFCgt\n8hEZxaJ49+56Ij9pjVIVZbmcLXYFY9iRzDAjH0plmD4FoQDKMG5qIXqIIvOJhFSSpeQrQZE5Q48x\nbz4J1vMT+SBn5t4P0rOyEYUkbyElsNmvMQSffii2XyjDclmzapIAm8hP/viP0H+r+Pu//zvCY8/F\n+alUv9HS9LA+e8af//M/5dnzl7z6zW/4j/+j/4xnL54TBscPvvgRDw+P1EVJ1ApDosGrCmVg6ANK\nOdngkb68MUW6fxWFFdlQvqddmHr5eY8S+YHIkcZzZUibdHZGkrWix6TQ4H2ufOQ6SetBEpCc1GZm\ndz6neePPh9YaE+0YuOZIzZzhOH+/ckyIwdS7lc9YlvX0GWPaZ4kQ816iUlUpnqF51p9XLnXezbiH\nihmIFCqjTthM+sA+TYIQZnMxfs55YMt7dK5cP2RujkTCfmAIA7/P8a0HPe/TwhzZRCbNVZKfQQ48\nuTKxqZfnUrNULIkyTVskBMP4uBgj65PNBFHO5Axdl6i9hQUSCcV5fJzgBrnwKWgmHZ02KajokAgl\n8lXbgPOCPUtmbohKpgCT+opD8GnTiJTF7AZIos+u64gJy5+X/GNDewYrAqxXNUotnmRH8+PZ5VkK\nXtNNNF/Q48LBJeszT1Ek5heG0RwgVZayidpx8YQkQo1DS8RglcJUsoALXYyVqzTgy6evjaM5toSo\nZQRw8ICnLiuOxz09DqOgriqef/ycv/hn/4zd4ZH/4r/8zymsoQD+9f/2b1menHP18jNOrz6WTL8u\nWC1qhsOOk9NLlDUMzZH/+X/8F3Rdx2q5HOHNPNqkHyKDEx/BFx8/px8a9s2RYcgsNgnMbdsz+EC9\nXHHz/pYQxCpu93gEZbBFxWq9oVos0TpwfXvH27fveXjYcn72EUYXDN2B11+/4rMf/JkgAv3A4Dxv\n331F1zWcn59RlquxqjPGUJUr8UBFqg2tLDJpW5IsCUjyPUqjizSPLQjsEYMmjhQxlUgUg6AE0RN8\n3ow9kYEQ+xkyItR4Fad7V6VsXRuf+kIQlZegFwNBSUdZoYhhoD3KkFbnHFYbfK0pFzn4DHTe8Qd/\n/Ef86T/9D/De8/nnn1NVFecXp9ze3vK43bNYbKjKmk8++WLcXI/Hltvbjro+RZkyzeMMaGtZF5Gg\nHW17JEYJhloJK9NgUUoTTWIYJ+N3F0Fbg4+J7a0URSnV0uFwENkTCpWo8wEhjqgQUGlNDN4x+In9\nKJtRSiSTZWBe1/PglddGWZZjIJj77LoE29tZf3z++Px6uYqLJA6Dy3Khgug99WKVhOqMj8mkFxDW\ndtSp2MgolBa3q3pRpvtGkLX16QUhS6HCgC2qEUJtux6MGXkPLkwWiS70o+ZTKcbA3HXttFfD2N4h\nBELf0bbtNHT3dzy+/aDnJnp5ruq8DynohfQ7kajnEOiEc08bfYIChymrMTkD0eK7OZ1IuaExQvRo\n+o4sNJc+SA4OcbppARL5Q26gqdoSOm5E6xxEGOHF3FPxqc+hSN9EIZGM2QwTvBn1BEfNM7sQAmrW\nTwG+AQnMF8/Yd0txZn4z5eeFed8gChSjU39GTRmdwK9G3ruexK7SUA9YvUApIb4Qhd3l+g6jph6o\nStmqfF5hkpaFRRmNj2CjpjICQ11cnFFXBbsEQ55fXvDFv/cnPB4eWWjN4/aOjy7OOX/2nKvnH7M7\nDmxb6RkVSkx/n7/8mNdv3rNYLFDesd1uubq64uOXFxwPj+N5CCGIPMEYTk830kNTUNYVw+A4HA4c\nDy1nmzNISdnh0GBsIYNaI7ggCctyJYFdnPYjQ9fTJuf4x8cty8WGsqxpmob7+3uWq5quO+B8x/G4\nZ5xsXSZ9lU7mxM5R2AWkSkUg6gxPZvRDIwxMndAMSxx8qvRTwpf6TCGRxrQSmzNhigobVIpGl0TK\nUXjT0Ui/LijCTBfrgxCnfJAedFRCZw9GiZlyFHMFLe0ikYCoyN3dDfv9I6vVhqurC3784x8RtSRL\n6/V6pPPf3u84to5nLz9lf7+nrtc8PDywXp1wbBoOhyPPnz+n6wZy3zxGGAbx8XXRyfBin5AG5SmM\nxqVNVin5Pk/8UHrqWWYSy7ynLsn3VMUMw8DQz5Nkxn0pRtm3pN83rbMU82aPiU/Wb17PubWR3WGK\nKkOSU1KcHzOHAsfnjFObZL7WJdHLj1Wjti6k5yorm6o/TVTS14shjLIq2Qr1eH6cE+KMNZldybgv\nym2ZesNM0jKT4PS8/ubIyxzuFK9OCXrLukLHgP39ZHrfftCLShOiSu04gdMk6EmvLcMrMUq7OS9E\nSFKD1MALweGdI3iBCowVHB0tG2oeShhjHG+iTM31fe5vpGATs1g+Cb9z8Ey9r6Iw6DTxwRZ6dE3Q\n1gIO0oUNIYi2S4GJMCQ4UGuN1WKsGn0ARMxprUlTzD1Ga0KIaNIEiiDUYfsBgcEmjczQ577fxGBV\nSuUu54jbzxdAZsXK/6fnszbZIOXEA2nvIPR5ldxt5kFT4JJy+mzGoFMfKngRxucRKsaK72mMUjXb\nspIs0AdcJE2iF1ZhXVjqQqZSFNZwfZ0GFYfAenVOVCWrdcl+1zAExaJayvsInr5r2O4OnJ6esqwq\njoctt7fXfPrpRyyXS159+Q/pHEiWejweUTqyXq/ofYuKkaEbRiizLOvRJBctQuOTk1OUygy5DSGI\nFyFY+s5jS5MqLEVVLWjbTuzWigUA+2aLKaHpWgbXstmcstmsBG41BYUtxinRfRcSYUKNG6IQumLa\n7HMylPu5+XpPSU/+P6XS9A41OWeAmDeEEKirAh/MmKzMK4550gQkNmtav1rWiIvi2KGMJjhHDIHK\nFgRt8Ikx/fzyisvLS4qikvvA1qB1mrpdcH1zz8XFBRHD6dklXet48fGnvPnqDauVWKWtVhsuL59x\ne3uLTZMLskTIew824o5HtLVkHZpVwhQ87A5oa7DajAHPGGGfomWjD6lTOQwDIcpajGmPMjlo+DAy\nlOfOR/kcTZKHCbIMfpGux3Td4rj/xanqdIFhkBFfwsJVDN6NPV6rizRGaCII5eucA21R6tS6Sez2\nqLFFQXvsJIhhpN+oLRoFaUwQUSarjK5YSACTosCgSzOSrLz3aeRbAWpyaNFaMwTPMPTjnis6a7m3\npY8vVWPfynT55bLGB48feoa+pW2a8XO5dk9wPrVBfvfjWw96ntQ/8FO2UuhCRlCElEGo5GLixZlg\n3qODlO0qyXzqsmQcNe/FtFkvkmccAi9kn88wOFCBybYzQvTSiyEHWJnvF4GgAvWyZrGspCc3dLjo\nxn4JLmKiQJDSO4vsdvsET1qsTtmvd7iQ6buVEEUwAtsOXmBGL5tJqQvwchMVtiAE/SToOZeht/K3\nVm/aKMyMvAIk4g1YVcpHjrnXoNBKnEFyI917n+CNTIFP/TkyjCWkH5sqEqsLEUdEWFTL3MnH5mpU\nKaqiQFs7MkIBSgulFt0lHipboYFlvRo38NNFwdCCVRZrTxjSmJKoYFEVkOjgPgQKXWCKEtd29F3D\n+3dfc36+oioUh/2Wq4tLAI7Hltev3+Cjo6wNzh9RMXJ1fsX7mzv2+5a6rnnc7Vlvzhh8IKBYrteU\ndUlZGDYnK3rXUddrynLJen3FdrvltF7zuLtmf3RcnF9SlBs2m0uOjWOxNry/fcvuKJMCLi4ueP78\n01FKUCQ7MpWuS1XmCovJy1FPUL35APYOKRmMeOEnGqlSQpAkxAeXgo2Qm4Q5Kp6f3dBhiwW2WIzJ\nWzZJlvU23X9VvZafkcTXcZrKoYwhmslEYZ50KSymqCGKibCxQswpkoB8szyjbzyGkjAoqqJmu92x\n3JwI2cIKKWTfHLGV3PumkD3Au0Dve6KXxPZwOIzrog0QdaQbGlQ0VHaaAiBTWhxDO6BipE5OKU3X\nstyc0B4aeueS/61h+/iA7z0Xl2cze78wEsoA+rZNLkuasipHazKAolxhbBgrnWEYcN5RlKXIlIIh\nAlVdUhSWYy/ogbiqerROsivUE9dGIdEkva4PdMeBUKReX4C+HVjVK1wMhCEwdAPKQF2UovcNPSFX\nzH5KeqrFUqbbpD0DI0mrSehAjJ5+5vgE8Lg/UlUV6+WGY9vQtQNFofEJmvd9C4E0p0/juwYVIzYO\nqKRBzS5c1w/33Nzc0Lv+/y2s/D8e33rQO2wfANHwCDNJskKhZ2v84Ljd7SAkRlBRyc2ppplXi8WK\nOSMxpv5ciKILin0nE9BCEr9HQeiilsw4+1UaJZ6HYzAVKS0KAyqgMejCMoRBMjOtQEkjXujPBowI\n3TVy49dZgxgjKoi+LRNttLbjgiMkKCUIdi8Za4ZaUrAKAVRBzuTkhxpSoAVhrJLevVIa7zxRR1kE\nZCg2wZPZKSHGcdEIgysxLlG5AB97oSbh/YBUEWhUkAb4yOhj+t6Uk64ms8yiMsSoUdrCbKPOv1fM\nrJtytRqjyDPy+wC5Tj51qmKI6BAT00wRlDD1FDKNY/ew5WQlriRt01OUQlJ5++6G7eOBzemKs8sz\nuU+c4uuv3vD23TXVYsn77S3OR75+/VesN0vWy5XoE3ePBG8oSytyhVBRFAsCBU0Ly15z7CJVtcGW\nFZfPX3JycolzEVvVXD675MWLFyKhqJd4H2n6gWW1lORhDksF9cS0YGLfPmVwzns7WfqQiSqT5VQm\nO0nAy2sp3ze1XTx5TgwUVnwiP6z0rKlGhCRDVXNdbQ6W84CsUChTYWc2FPP/L8tqrAzyZymKAqMn\nKC/46WsMYKyh7ybS12KxQBuBmZfLkxECHoZOzLKNIBrHYzv236VKD2l6Bdw9PALi5HJ7c0cIgfX6\nRPrrtmSzEf1bURQUsz5c1lJaa1E6jeQJgT7B3PK5Pqd3rVwfDUVlqBbCIN9ut7SJJVkvqySbihRx\nIaPIvJCDnJPPu6yXYqAeJKC2xwOlKbGFZvCR0/MzvPe0bUvf92w2pzRtI0FRNgViDHSuZ4i9VOlK\nCWleCQKlbaRpW3Rhx5FJ3vUc+57T1UIKgE4GPqsChkTeWixWGGN4fNzj/cByuaRpGtrdAzF6gpPe\ncfTixOWHHqtht3vk9u6a0opt4Jevfs3Xb7/icNh9g7fwjz2+9aA3HA6JVQTBe4KXkUI+TVEW0WNu\n2ErQyebEyhiC1gxx1jBOa0f2YdkwNTIyJhNSjBVvSZOgFmMm3FtreR95CUaSdIIk9FVJcJs2lDhz\nCMgMprG5nGCmrMGb4FN5V7LpGGIUndpkd5aHLhqMNgQS++2DDW5+zLH9/F6UUkT7FJLKfQw5ZhZA\nOUppBZ5vbG5y5F6FYgyR2qAkfOYQmZqXafNVotVKDU1QMbEIo5h8Mz1XhlqNseOFjGPUTeOd1KxX\nmR+d4e4PAmhGDpzzdM6hbYkCQvTpNeD+bktVLri8vKIsTUq8ZKPe7/doW4n5QZIIVOWCzUZGX7m+\nQyFuKmVZUpgVIWQ/ywXv3r3jeDyyXNYs6hVnZ2dYU+BczzB4VqvV6JQCk2Yw0+WfBD3UWPV9eOT7\n6htBD5OkA99kxH04jeNDnZfcRxPsrbXCmG/Cmya5kMCE1ORDyEvmG9dngt2ffoYPf2/+VdbPU63h\n/D3nQJPhxWzKnM+nUnJrC1zssKag1xrikHqoSXoQPT5I0NNGEo+irNCmlL5vIf63PvRjH8z5iDGp\n541PHpx2XCti0+XIDOucYETEgm+EN6OGoDE2UtVZPxlxvh2vsYkKF6NscEG4CV3TjvdKCEGqfoTU\n52MY/wZyD82Nri0h++NqcTAyRuOjFQQF/yTRCiFgQkSbmMwqNEYVaXafGGuo1Arq+p62PbKqF3jv\ncF2L0pHd9h7Xd/iuwRqZoODVQOc7umPDzc01Xd9we3vNzc0NhdUcj0fu7m4JoUGpAaW+aVP5jzm+\n9aDX7rbio2fEH9OoyNAdkvu4BLvVaiX4eMLY1UiRFUZa7zxFkReEdP7Q0nyXiQdhtNPRCMnBFFLZ\nqRTIolIy4DEG2RgVZMFqDnp5AwhCwwClUUqPRtWeKFZVWs2CbyIIBEXQCp0bxDnQRU2ideRPR8zV\nVJr7l3t0QYltD5B+V4JvVKl4GwMEEhzSlHilY5qhl+HPqSYb+zlqyqq991NFlZ9OiUlYjB7lU1CL\nCh2lz5GzZDkmu64seEXlCi25w0TZjJVMgUvnQSXhvlQpQqKY/j8nBJmsECNjFSg/U+NrxpRouMEz\n9I4YjJBuglTMXSeB7ebmji+++IJnV8859lshN6nAel2NFO8sBj85OWGz2ST4O0A4J6SehFaWerVm\nv2vRyrJYrPg//91fEuLAyckJtqrF37Qd6NqBoCJFUaJ1dqYHa58SjZ4EGB3RcYKu57+TA8aHAVE2\nyrz5ivZyHvRI5+6bFWNq5I76NOkTTwHum0Ev25jN14k8t2b+tsYNND4lWM2DspEIMpLApC0hPeZM\nzBKIVG4jrSB6J99oMY7vUh+pqEoiQtuPUQmEWwoDtlCgTZmE3kJwCy4K8U3lUTqKwYnNX70QpKnt\njqJjywnV/ii9fV0QohP3FaVwUUYTWWvBWFQUJKnKUhg/YLI5ferJ5XNQlqU4+qSAWBjxzJRgN93z\nGilhvfeEFBitFoJN5x26tByabmTAG1vSDZPXpzJgClnXPjqi1/ggTE0fg5B0klzBGoXobRmJdxrG\nUW5Ga1yCItumoWsOLArL8bhnGAbq0uKbltWi5G67xceBbdtyaJrR8OHXv/mVEMeOh7GfHBUYHVDR\nYbUMRv59jm896J0uJLsACTQhRLquoW+a0S1cr+u0GSsGJ3ZMWksmFKLGGmFVyvF0HpQER1kwBkDn\nCex6bNyKPiUHonQkH7ssxM5ORNJyEkZbiHF0v5uzJMd/M8kmMJPcIG/eMchk94ntpcYqNm/yifoi\nP9cWQjdmYfnIPcGIl40i6jF4BO2ebJ7y2h/m2dP/xSTs+uZj8gcTYkY+PVJ96FSFhfF38vfCCJQA\naaz8zvQzPdtgUzALk/A3YS9jZSxF7FM/1DjbmPPX/Fd6UZ6h91TVgsfHHdYq6tJy2Dfp8hZcXn3E\n+fkl+iBowE3zPkHn9Wg7JhXbkqzLUiqN2hkcbXeUoaknJwnurGlbJ1PUreY73/mMGCNN07DfdWhV\nUq/qxDBOvdDkkB9CwIyTFKZ7KcN2Hwa3D+HNDx+nRij6qTZKPbkXGZ8bMrs+nf+YxeaMTN75644/\n0xPkNH9P+bW+eT9N7OTf9hnmhJD888lFRD15/5mUln/HOZcgzhVaW4IX6rtCYwp5v26Q5Mtonfrp\nYk84+EC1WAFiCB2jMHVVDNiqxMWACyGRr0TTdzweMYUME0an3isRHxQhvReRQ4qG1NrMVIxj/zND\n0M71o8G09FpFG+qLguhCSg6n66M0VFU99tGkD+yJg0/6Z+h6qUoLa9GFpm+keitKQ5lmdw6D7Lsh\nQFEuIVX1MrNUKujgHXhh9eZWUd63vJcp66FPvIzYoyyUxhFMpIyBqlC8/tVvWFSWw+6Orj/w+vVr\ndrsdTSvTJNq+S2QmzWq9YLfb4UOgqizNMKDV5MX5ux7fetCrXIPFQhaHl/J/LdwAACAASURBVJpy\naTlPU4mttdiiSlWYovWBx8O9wFCIBqTabAiHHGgyFj31g+xymaq6tPC8LDrHtIjyVx3zv/NGqsaJ\n7EExal6ipJ2QbrQwDKJNikDI1mOysduyGKUI2VvOWkXfuaSDeRpYBA4VBlxmQilrZPAiYSTYQIJI\nsOMImXGjSDRkrBkXlcq9wQnLnLwZwxQ0hIU6+ZIqBTr9UWkY7FR5gUy5kM+RfkDOACaSDFgtI3TE\nxQbi+Bx5k1eQhcXMe1Zymwqr92lgU8wqhdl1V0oRPFSLNVpZPvv8e/zL/+G/Y7koeHZxyVev3wPw\noy9+wrNnL2g6z3pzQtc2fPzxp9y8v+azzz7jcd9yf3/P23dv+bM/+65UerVo4FaLiuAch+YoJCSn\nWC03dL3nb372t9zf7VidLrDlgr7zvH0nk9R/9MOf8MWP/5CryxfYNNw0BqF8hyR+9kxsybzBZ5p3\nPq/ze3cOIzI7D20nUyvKsnwCgY69sTnsnu4D58ITnahUNG7W95uvmdTLi8JGBjC5eo0B58V43dop\nYBKjmMjP1qRc75BkMMMTSn6mrueWQO5Tii9jl/pUm1TVCjwtBgM13imKQpIrEZUb+iHPSBTbw7Y9\nTlWWAqIwjR+2O0D6iTe396PLS1VV4gXqBrqhR5WyH/RRJqugYPADrpOgsLLyOXM159O+87A90jQN\ni8UiafcUIVqa1tO0u1GbGzE4p2l7qUKFPzBZDT4+7Mbz6L1j6Ns02Naig0Erw+A7ul4kJ13nqcuC\nvh049kf6VuZplmWJKQra7hGrJRHo2j1WgzUKHSNVqTFaEKYQe9pjQ991lOkzvv36NYf9DoLDasVO\nR37+85/zs7/5axZVQfQBbRWDO4IRA3FlNIW22FJR2og92whDevCcnF4Ro2LoDmwWV7NRS7/78a0H\nvYW10q5CNnmNYpFo/mOPzIshrIoRNTjWlnEz19qjhwPtsR0xfZsgF6kEFF4HVDZuHXqWi/XocJ5v\nxtws9zEm+ye5qQbvxmGqtiyISiq6IkkF+r4TWywtQxijET0TAbRSadSPeNRJxiKMODc4SmupihI/\nzsZTNF1yfLEWUxZ4In3KsLQGpd2TzW0IDu/CZP/VT1PQheo/z5YF7subXVUtZotfpcQ+Vd06YKLB\nmJRRh7TxJZ2XRpiw43nTU2Y/MvSUGkfi9H1P1/RTLwmNH6b+hvciTs/vfV755OQnMI1ImQTvcs2z\ncHjunlMUFc2+Y71aE0PHR89eEHzHz372M169egWIKPl4aFlsKtq2R6WsFRg31Kqq+Oqrr/iLv/iL\n8R4DMd8m9ZrWqxWPh46+D3z16h2vX78hKIXCcH19yyeffUrwkT/5k3+f733vx5ycX+C9SGJWqw0a\nNYqNrTYyTDknOSmB6vvpXHxYgc91WhN0KVMn5H6byCUmmQtMFnuJpZv6ePL8munpVRLJ2/F5c+/Y\nJ8kNVliEEjg0xhoMMPijsMa0wYcsDbC4frKjmr//GOMIUSolMGjvBnaHPcvFmj5Nbq+qarSI08aI\nS1AU0N8WhUh7tGZ/lBFIi8WCw2HP6ekp0YEymirBl7ubG5bLpTxfquyePXtGt9tzPO4E5rSGdujR\nhaVzA4fr9+keK1guFmla+5EXL15wOIgx+cXFBe/evePm5kbW6iDTDWQAMLgucnH6DOcct+/vxz3p\n7OyMtm0pqlrm71kre0y9onOebhgojSSj3nuULeiSa5BSMuZMKUPUGjcEFtVSjJ+jl3k1ZWC/21IU\nBqs0ISUXrmnp2gOm1qI9dT19d8RrcAqWixrlVAo8Pd71vHv7Nf3xAEkH+3/8m39D3x5RKmI0rOuS\n4Dxr1eObHYuqwJoCb3VqywTQWQCvKRY1LgR8gMIE/CCIjLUaVIUKkjD9Pse3HvSILhUGHpSCQdhB\nKk7TtEHmPxkCq0RVHqsqozAG1ic1AT1St10AfMSpwBAGSqWJVmExhL6hjcO4ObStBEyfNXDJTSFD\nmtoa8JHQOwq9IHoIfQBt08DVCEEIODqAyaJOZfBuSD0Ck3kcEFNLzDuiluxUKQNG5uFV5RIfBlwv\n061FCC9wR9c334CEJr3dRFAYAxkf9IeYVWhMP8+/F1RgCE76D1rgqxjjOD2ekCQk6bV9njpQ2DTe\n5CmhYa7vmr+nsWLx0mCPPmC0RlsjnxvGcT6BiB8c2EmPpFRyoI9xJDGEmXHvPHh2XUfbPHL/+Mhx\nf892dxiDrXOOu+0DVWfYnFWUiU1rjMhglkvDb37zm9H3UClFYSu0gebQM/SS7bd94HDwHPYd93db\nmmOHd5HjsaXvHEYXXF19xMX5FcvlUtjJKjurSO90TAjC1GvN5A1JPOYw9ATr/rbrPD8P4+M/qPLm\n/5fPV5wF/XGJzh734SH3DOSxRHNCRYxxdB2ZtIVyZMeT+XvJj+37fkx4MmQ5JoIfwJ4wQbc2TUnI\n8oE84DlPMs/wX36NbEgw19D1fU9V1ux2u1G6pJRKTMRFeu5+1BcqpTgcdpRlyape4FxP1zW0h4b3\nrud0LeOMoo50xw4hgsd0f5e0B7Giu7p6xul6w7Fr2e12qBC5vb2j0AZdC39gGGROZ4iRIURIjivR\ny15Wl0K26rsWFzwmaPb3j9hzge3d0NP3LcfHLacna3xzZD+0eNdidZoZWVhcaJEmhseHluicIBC6\n5auv3nJ3857t9oG2OXD3/g1f/frXrGthmcZ2T61JlR+U0aNNFGmTCRg6CDJcO6RKFWXJRFKFE4mV\nEeKWjnlSSMTaikJPk9Z/1+NbD3qRyZsRpQhKRKCBOEJXQvwTSCNpoAlKBsRGLUSV0li82EFI5aak\np2eVoiw1hU4ZpBYoLuAokli9D056Uxq0NgTfJ2gyYstaOk55ERqDGyBoEX3aqsRo6HphfEWfdTwq\nzaEKYAxER1DgE/Tjnfg2elTS+hl0VIRhQBXlyPATkb0m+sgQHd3Qjps5JCILAikpGGGkJ4FRTRui\ntblflga46tz3kQpWRYUPwjRzMfH/gowdEV2hRidmptKa6IRxiwojvBlhdHdwbhLTEibXh7zJRT/g\nvB97Hwxa5vk5gYtHfZ/3oAqGwY+VudEFQ9I3iWHyvK8nJKOiKNgfGh73O7qu4+3bt3RNy/pEBuhi\nxNIpBMNyY3FSwjJN2lC8ffM+UdWrcUK1iHjd6MjR9nv2B8ftzQNv3t+wO0p/pB8cbT8IW3NzwsnZ\nGavVBlsuwBbyOkjQ0GkIse+72fWbFnj2jM2VGTyF5n9bUMqElHG9xTxdRGd0/klC8mFQlcfkf8/7\ndtNXCWozZnBUBC8mE7kv1vf95EoiTdFx5loI6bORjJ5VFnFPrN3CViLSjnFMDpyTNoAxYrqe2c95\nLptSkbOzM+7u7sbA2aZBx/n+z5ZgbSvrqmkaTqqSh/sHnj9/Tkx2bkMMdEdhmq+XtVhvzaA2W5cE\no2gOO/zQQfR0TceRBC0XWpi+QYYwA7THA4WxDH2HC57762vutw9sNhs+unqGNorTzQm3t7ccug67\n2siasLJDKqUwFg5tS2EXVKmvedzJJHoNrJcVx/29WLH5gbIs+OhyzfbhjqFrUTFQFoqhfWR38HId\nCoGIh67j/bvXdM0RP7TcXL/juN/SNgcIHms1JgbOV4a6DBQmsNIFYmsna7pM7lXCZ1BE7/AqgC5F\n+6uj9OkUMiA2ykglgwKdkiMFMZhk+Vb9nnXe/weCnp7p1ACI0j9TycoqIMxDkm1VSBCP9LfyFF2P\nU1mwLquxNAZbWDCaY79LSn6wpqSqBD5tdnuUNaw0GF1gdSQwYCuTXFUUpogonTz1ghdfguxDCRx3\nMQlFwePxhTi96yTDWC5X8thhoO0doRaIDx9omw61HASajBBUZH880NQ7tBUYanDQdYl9aRRlZUcv\nToCqfjqVYQ59xRhH1uRUAUxd4IJi7MnkI8YoQTZ6nO8ZUm9QBenpOdcz+B4/DOjUUx2cQ1mDSga8\n0YfJ5ikKNzUE0S+FxHA1xuD6fgyIpZ0syoJ3uK4Da4kJlg4h4ItK4O70+bbbrTDkYkwby/QcAG3f\nU6Xexmaz4c///J/yr/o9ljhODv/44xcsFhW2NFRrzdA2Y4/o/mHPz3/2dzRNww9+9AUnJydUlQxn\n7TsJdtn1/uFhx+u3D7z5+pqH+x3eR9abM1brmrKo6AeHNSWFXaBMhTIGY0tAixB5cCidKmPnCTr7\nEk4ByxYTe3J+fBjwnlZ1Tyu/XDl9OMLlKeEkkNmv89fI00WeHkIICTBW/fPX6vb7qYpUSsgffS9M\nR5WqNCPOHV3SneVRSqOIO41+GpLVWwiBq6srykU9ElcObTOus2GQnl5ZluyOBxGoJzbg4fYg+0NZ\nMoSBRbHg2B05HA6s12sG1/H161csl0v+4Ve/IITAZrMae5p957i52eOcGJNvNhv6puXVzXuyybdA\n/iIo321vR5/KruspCjtWkF1zR+vkGhzaA4tywScvZbLH169+Qb2qOW6vOT8/5+zslNdv30iSFr1o\n9ZIped8d6B81Dz7gfM9yWVMYQ3Sex/2BoWtxfpCBue2ef/V3PyW4gea4Z+iOmOgIXtocdV1htEMn\nQmBRWIyWK3xiA5sTjz6VRCYMPV07sD5f4Xo576oUo4zRkaVtZv3nAqWlOPHGEhHJ11jYKIQAZ8Ra\nD4V4gEax+ou2JCST/N/n+NaDHmTLpIn4YZTQ4IMKAmFGBHuGkdYP0i+Z6/LyrDyBUmJyRY+o2GFT\nj0tZ8ENLCBB9j44FxhYYFbAq4ENEDYkzqZRUeMn7TcVIWdXCu8jsyuAIYUBX8hmMiqg4EHwgBDju\n9wLBdY5u6AlDGLV6XdPihl7GbUTBa5uuS8SYkCqFAVsa1usTqrrG1sUI88CUpc/7aPOM3aZhr/l3\n576AxMmeDOZwkUy9UCoS01lPtaBwxNPw3nzrxRDomxaVoKKYWG0CuZQikI8OPwxCMtIaFSN9K5/V\npcdl54fsSrJerwl4+mSua1YajB3hqOx+Mbd/GiUYISDGATKhoS5XfPe73+WXv3oBwT8xnJ5G9xjC\nIOy5qlpQFDIaZbPZsFwuR9jMKIHRyrKma2VaQtM0kpGnfo7WlrquWSwWLJdr1usTTk/PRk/Jruuo\nbDFWIFLNynmV7liuWie25jR38ik7OP97Dnn+tqA3T27mPbQPoUulnkLU+R6bv1Y+hCrvCXHyqJy/\nVm4dZJu6LN72cXiiqxsTmxkLcWI2zu9feW9t247nJAeg/Hzee5qmoeu6cep9/owZ0sz2WUopSZ6S\nm0rTNGOSJL6PQ5qy0VPXJzSteM0ul8Io3+8fic7T922aXABt26ReuQx7lWow4FygTi4+spQaQoSz\n8zPC3ZEvv/wFv/zVX3N5eYlzgY/UFW3bc3v3Bud6PvnkE7zvadojfuiT6H3g4mzDMPRs9/fc393Q\n9+0oRn/5/AW/+sXfs93eowiUFpr9A3VpKYynWgZ0FAcjYXNKA895mXDvuz3OC3luWZW4vhHLMW3A\nwOJkwaKSfmj0Ip+a5vVFytXiSUvCGINBoM1IIhjN7illZOyYimnmZmJ3ex2INiIM+/+fB72J8SUL\nHa9kXE8QwXMOhFop0ErcTJJRbnYWkYUgW3OMEgC9D9IbIVIV2ehWekgx+crVVuAUoc8HVAwYFK4T\nqEMXFlL2SUwVSkzWJVF4lDbBgprErCIKNIdMgehdL1mwl00tBkfIo1yiZ+gaet+jMBSVpawEDpCs\n1QlppiwpCkNhNMVseCRMm1eu8PIxD3wf9n3yVzHgzQLkXAEGmXagFVaZdC5jsokbBGb0MAw9RlmG\nztB2LV3XoJPOLDhPIAp05AYx9E2T35XRY1EfnKefWRblAZPn5+cidE1w2jAk6cpyOVYTmSST3WFk\nkK0fJQYgWaIPnrosIWXsp6enDF1L1+c5gxFb6GT1BG2acJE3ydzLk0QlXZeUzU8MQ9F/HfYNwyD6\nyyxrUFoMtcuyxFYy+sZ7adTrwiMGzxqjLeIf65MReb6OU88t9y7nPbPcL5tLDqY1EcagNxdy53M6\nvx/mUKbW0naIicGstOjLfJg0omPPNgwEtHh1esYp4z5XkB7pBydLQeccQ+9n00amWW/jfRnjeI/P\n/985N+4Fx0M7BirpiU4MSTeEUQ70/v17mubA4+Mjq9WKGD1lWZHHCglz07Fc1um5Iq7rOe4eWSWD\n474VlmX0jhDkXgiDo+kbdrsdVSFownJRsd3ec9jvqCrp7y2X63EiRseAItB1Um3e37/j/m7Lcrnk\n7PyE1bKgHzxaO7QKvHr1S4KHly9foktoHm84Ho+8+upLDrtH0bY1e6Lv05SZga49sl4vk6+w4y9f\n/xJjoS4jVitBcXpHGI6cbJZUZUG7P2ALjVXgek+tLYVVmMpS10tMqvKNMTRHPcrDvHcYY9nvHsUw\nXhvq5QKVoGprhfQz9x+NSmDrQqfJNmn9+xjJZucRgfuJopOWlrem9z19fJp0/S7Htx70PvwAo88j\nE/6vVR7gqHDeYZK+S8eYmuhyEoMSPgxm6vmhPEQ3mhiHIDCRKQosoosbUj8pgzk5o55r8JRSIoKV\nxgTgk4osYpUsbKNBxTSeyIt9mVWFmDQnQ9zFohaxrXNgNcPQy4U1UC1L6o0I8W0fsYWiWi6pV0tM\nVaKtGDlDEvfCk8xYLMmkCs6SARHbTr2XTGeZguE0XFIphUcTvBNLNq3T2KVh9NRr9wdUhKEbhLHa\ndhwPj0Q9CZ4nwkKk9dM4kXEaecz6pEiexGyMYVlZYjRUVjG0A7uHw/i+Tk5OMOnc5snoy3qTEgqP\nCYEhVYT5fNhkAVcoGWtiItSqgNDTpr5OERXLomJVL8F6DvGAUuK60bvAcrlmt2/wDqpqRQguTZZw\nFEYCr9DjxSdQpWkbziU2XYTgNa6LuNbhOkehnMA8yUNQmYAtarz6plOJSEbkpy5VD2ZkJ4sJm1JR\ndJIx9wCN9M1iJGaYGVBxgn794J6gBCPzMjnM+KhQgUn3iYGYExQ1Mo5dygGfzENTmn5wqbcugm3n\nBgwpKPmeEOfyiTBW3EopdrutrN2QhtkGJ5BvlDE+Ruk0YmYYq8iuF3gzDNKPWq/XlJVF+ZK+3fJ4\n+5ZuX7Hb7Yh+4OJC/DKPxyPEwMXFxSiQVkbTPBo+/fRjTDzQ7e/Ybbf0h4qqqvD9wOG4J8bIer2k\n23Yszs4og2J3/WuOxyPV+Tl+6FFFA35gcI7ddkvXdRwPB/iv/mvevPoZ+8MDD/ePvHz5kv2+Zb9/\n5ORsw9nZBV+9+pqiKDnsv6Q9Nlhl6duOpnugSDrldSWXfLGUpH9733F8uKPB0DmRJCxrhYmBUsuA\n3fOLJVZXrFcVsRu4//qeernALGu0jpgiGVFb0dt2zhEGJ7NCE3SrNQyDCNkvPnqeIGmViDbJ+Lsu\n0T0U2uCJWCUwdlQeraX/KIhmhtKzRjkhTCGmrcyPMUHl6QG/x/GtBz2lkgXZBJYRXD9lnhlqCT3a\ngdVWfDwyaQERTwY/jNlvXsgRxskACAEKixHmEOJ8AtLn80SGvAEvVgSg86RKSEYTRaYMeXr/QrEx\nREInkFqhFAXZM1Fo8FnE63ciSdBACdSloSwULT296wjtkc36lJOzEm2XNKYgVpZQlXitUClDzYGl\nsGlOiU+sQ1vIa6HGrEpFNRpIyzmdAnnMmXzaAAulGFrB7KNPkoCqoigGghtow8BwOELo0L2jUgUL\nq+ldRyQRIkyCWIf9CIkprcaqWa5tQBeaqFMPZ2Bi3g07qhix3o/6we56T//4QOvEnaWsLNsve3E/\nWVZ4J4F0cJ3Q9PHYoqDeLPj61Z7ucKTf7/n++gWhdHCe3tfDgffXt9zttnz83c+5/OiKYlXRuYFn\nH32GcxVNZ/nbv3vF/tHzoy9+wGa1REWHKRwmwLJeUFRrfvyj7/Pll1+KPrA65WRzwfe+90M++/Q7\nfPrpd/mD7/2RbO6qYLVc0HsZm+JaxyEINb62lt3Dlr7vxyShrqUKIfR4PH3qA5WVsD99GKAsx+9j\nUIk1GdBBGJTt8UBhKwYfcENgs9lQ1BVd141T2k82K3of6HwgKC09PB9ABQoj1yYqGHzElHLf3ex2\n9F0Hg6daLLi+vaeql5w/e8mbd6/HytEwsFyUhER132yesd01rFYr7u/v8X1HVZYcj3uIgdPNAufE\nZb/vO6ICr0tMseBwOLCqFzw+PrBaLoVC3x/x/YAPjkVV0u5r+vbA8eGGtn2k2e8Y+pbgHV2z5caK\nJVjfHYiu4d++e8snn3yCCxG1WKBN4O0vpJfVtw1aW5r9QFUtqGxFcAP90HAoFSo67gNAsu9Sluu3\nkmw+KsfD9h2eSF0tk3uP9PTczV9i4x2Xlaa//YrT5TmbTUDpd3xy8WMu6yX18hxV3HF5ecnQLGma\njk8/+w77/SMhaExcoqPFhyP9cAdqSXQFu1vP+vyc2+Y9n59pQuMw6gwfa7483mHslsP9NafVgmu3\n4bR6xoEjR7fj9PLZWDUvFjWx69hvt1xtLrBRPIKFVCL7qadHJSJdESM2Q++AKQqCES9irTU27UF9\nJ/3aXMCYIAb7omPOsGaG4JWY7nvHIgo0+vsc337QC5E8QHAMVn702ZFqKo7//AasN6dgz4Pe+Pwf\nQn6okQyRdUPZamz+3PPe2JwoMIdeMrQUYsTop9TwJ+zJmPDQD97PEESK4aIMl8WKdmixEucPmTGn\nMKoQ/DuAQjRzM36DsAnj0+cGkX0o9TQrUkqNsFUIQRKC2fvKcNJIcEhPaYxBE1mtVrReqiDvHC74\nJzfR/BzOafG/jU4ffXoN54khzRwMAV1I9e1Tb0yRekfDMInR3YDVCu8du61sSgLZOjCWGBxD9LR3\nLf3QEroBoxXFYkG5slSpr/LjH31B41r+6md/wz/86pfs9ztsVbI+2bAql3z80Ucy8SDIgtRejK2r\nsmT7cMMPf/A9nI/cbY+UtuCjq2c8f/6S5x+9wJiKn/zhH7PZnFCWNcfDI/udfJZlVbJZSy+kT44Z\nZr1GlSX946MQNrKEZrHAGIUbutQvEqips4aiKCnLgsPukGA7h3M+CZsVymgGa3jcCvPQBXn9tjmw\n2qzpuobHwyNVVVDZyLFrxT1IJxZl6u1qXdI3e0mk0DTtFviYoX1kv30k9B16X3LYH1nWL+iO9yzK\nyPX1O5RSLCuNqU/R2uFcw+vXf8/+eEiOIIaysux2A/v9jsIogjNUtaVrD9zcvOfq6opXv/6KqhaB\nuE1ksn6xoOsbtvfXuH4gBI9WkWHoGLqGOHQyHiyIhq0sLEr3dP2BelGBGlidLFD6CluJwf3gOrQT\nqFn0ZkKt7/qW47FlWdV0XcNiWcIQ8aFHa0thDVVZcn7xjMVigS0MKEc7fMJiVbNabSQJRRKGf/bP\n/0M8t8JaNUtKu6HrG7CSFHin0XbF1++PWFMwoPnk4+/wsPsF7eHI5cVHPNy2GAruH66pqoF+OOCd\npW8LdNPQdC3vb47cvrmmNFfY8pTb2LA7fMnx7prL8pT9sYVdjTo1BBS393ecbk5YLBb0aZTR1dWV\noDTOj4xxEMMqk2z/pn15zg+YUCc97pHCBZR9VSBxpTObNxnMB9I8QNH0ohXRiadpdsn6XY9vPejN\nm9U5eGV4Mf+dY/7iO/c0yAHfCDJPGvphZlOFGq2Z8iEsx2/6F/42Cnfup8xfb7QXY9IryeOnXori\n6eQADXRdS7QRbzVFVaNry3KzTo8XeMlqgw2a4CMu5AlfauzAGSP6mqGbnTOmzzAmAHouY5j1fmbn\nQCmBrZQ1qKDAg8tm3yRnjbKkXNQwyITo2Ergi1p94zpmKcGT9zE75tcwE5CAkaE3P19FUTAQKfKU\nez+kflvgeGwxymCMJmpNYRQecNHTHRuUUSzqigpN9A6dNnJ5r55PX75kuV5ydD2mLPjpT39K7D03\nt+/pe4fvehg8D9e37D76iJO65vmL53S7W85Wp7x7/57Hu3uMj5ytN3z2/CWffvYZYNDeMTQN3e7A\nsG9ktYeIr0uGx7snCdX2sJMAUJZ412OyNZUfSEAfvjkQXaRcLvFDQ+i9aES7Dtc5nJf5f0K38GAL\nMJrtbjdp5qJi6BqOzUMyDuhoC4iuwfmOohLIVBAWn/oxJlXiAYdi34hEp6BHxxajHYfDnk9fvuD9\n7df85t/973zv+9/n+eUS5wZub97TH2457B+5u7lmsVlijOH1zQ3f+e5n7B4O/PKXvxRYyzv2uy0n\nJ2usUez3O371S5lWsEyjh0L6vilEZjB0LVZLn+nQNOz3j/JZ/cByWad7EAgymOdw2PF8cYkuAuVC\nc371MW7ouH945Lvf+QEq9qAcSgesNhhd8oP/5A94uN8ytANNu+f0bIV3HSBsZd9HBh8YvOiBo4Gu\n61mtT1ltllRFzeAdLg3+Fjs+Re8d1nv2jw+46KgWga67pa5O2JycopWYL0RXUxQVfTcQAjw8PHI4\neK7fvKLtt1w+W3Bz/Zq2gZv3R/oQWV4UXFYd929vUf6ed7dHDnXk+z84x6EoqxptPO9v7jhfXVAv\n11y/ec1ht+fTTz9FG5GhrNdrgYIBZm5BkF2gnhqL/7Z9edpLw2g9KUw0RYx2DIYh6FHKIoWQGH8Y\nKwYd8ZtbyT/q+NaDHqhp/t0YWD6cGj7XEJH+ZiaifC/PkasL4btmNid6dhFQqKwp0pP9VZhdsMk1\nRI1O5qMDSKHl3zMyhTKawffjKI58zIPoh73LoARy7L1HVdIArjcrbFGJP3MEKzkgFk0MFhsDXejJ\ntOgnz6tzh1HPQiJjVZqzo3nQU0o9QcdzlmaTdowgPoMakVMYDYUWMk2sHdEN+E7o2NH78bXmN/2c\nZfg08E2+mRmKzu8pE0dELC0/F1ae2HIH73BDj17UQu6xJs08jIToCUqkFc576ioRfIKnbRqMhtLY\nsTI/PzvFDQNGaU7XKw7Hlh9//4egNG9f/5S27SnKisvTM9QwEPqOqaEd5wAAIABJREFU++trNrUl\ntD02MH7983/yp3TDwHqxplKGy8srjru9IAsuoo2wT1WImEHIQHO7r4BolWq1piJiYmBwAypKIrhY\nLEWCUhpWVUXfp95o21HFiHYOHQIqaUEbGbYoo4FQ2JHdqjg529A0B3rfUhaR9+/fsH14izXwySfn\nRN8x9CK0FoJRpKgWMmFAGQ69XJjD4zXH/T3/8IufE6Kiaz/j/ftrXn31NauFG40G3r15TYyBrj1y\n3B/QhWe1WnI47FgvWvqu4bj7irK0rBY1se4odGS5WLJZiQfjqipYLDSFLTkcdpycWAiOtgo8e/aS\nwliqOmnVjkestdTVgtVmDQndsAl6f/PmNecXp5RlSXs4sFwuCVFkJc0xgBJWNsqlKeGKL7/6Dd4h\npvJGJEplLQSSGIX1rIKh0hVGyVihsquwZcHu0NIZL1Ni0qLbPh4o6sDj4w6jHf/w67fynhaB5hi4\nu90xdIZQvCcEcO0Zh32L4w2X56dsHw6cn73kf/1f/jWff/c5f/JPvi/WabHir//mb3n1xvHf/Lf/\nKX/w3QuKHyu6Q8W/+Jf/E/8Xe28ebFt21/d91rT3PtO9943dr0e11C11Sw2SBTKjHEk2FYm4jFIF\nsizikg1xlcuUnXgCgjBljLFTIeChioqnQKjYMRgRl3FsI+JEBBsPCEsIhAVqaCGpu9/rftMdzjl7\nWFP++K29z7lPAmGwS0kVu6rrvb7v3nP32WfvtX6/7+879L7n8Ve+ChU6rs0usqDj3/zsh7h1fMKh\nWuBczel6w/UXb/Doo4+Sohg4zOeNdP3ZFOa3kEwkEHb3nN/7/O8XsDLfv5dYJxKp0SoupSAjrzTO\nnYUIVI2qtt9ao/e53/ScKYm9WTakDFMK8njxog/T/ztXn+u8xk7wfNdwfoMZmW3T65VOckw/J3Ou\nZf50Cvf5vLB7N7NcuprRkmuC+KJYg8lr6XPQaFKKbCxuWeFWc+rVAuMcPkaqbFHZkPtIvz6FytMs\nllS143h9QsySPAGiIPB9QBeLKBkET+9c8HAUpF3RMHa1FFi3vMvp/Q6xdI3WoAvhwBiFURCSmMFq\nawRy0Kpk0IWJsLJ/zfaP85B0lK5rpLObwk5UiqbILOT6CWkn5EzWAiWRMjon1se3SxWucNYSwiAO\nODliEJ/AkIIkuVuDcXOMQsgAG2HQXThYcePmS6zmM6nWteXS0QGg+b1v+QrabuD09Izj0zWVtfR9\nS1055o3l8WvXOGqW6Pse4anHD1FGJAir1Yq6rmnblptRIoSMMRwcHEz3xqKZsT5eo7J08IxQsNZk\n3yFGZwaiR2GojcZvT7Fao1JiODtBGYWJma4VtxAdIykFyZczRjZCq9DOYFc1VdWQkpC6ZjahXCY7\nMWrYnNyQTXA45uQ6VJU8czoXg3Vl6HpPVpaQLc3qMgC3X/j3xOi5dklx+fJlct7yyNNXeNOXvJwX\nb91maEUe8vDF+yYDAd8PXL1/RR82XDg84vj4Drfv3OQ1TzxF37ccHR2xmi/oum7KtiNnKpsZevGq\nPD42rFYr2naLcwcE7wlhS1PLsxa3a5ytqVxCxci2a6e4oVA0iD/7s79MM5/x7LPP8tjjr2BoO27d\nusOqWYCKeN8i9hFiitCuPc7O+NSnPsXrv+DzWW/ucni0oKoEcjPKoo2hag5xqgbt+MQnnuejv/gM\nH/rQR3j66cf5kjd+EQcLcWn5zr/8PfyJP/kHOTy8wPPPvcT3fd//wVu/8gv5XW/6Qu6/epF/+ZM/\nyAd++ln+/F98N5WbQ76Pv/O3v59//7FPcOEivOXNb+D1X/Cl/M2/9X/x+jfcz6Mve5zHX/k4MWje\n+96f5cXb0CwOWG97jpoV9XzF8brjyS9+LUFXzBvDSRt42ZNP8X3v/d+52Wfe8MUPc/+i4eLly/R9\nz6/86sd55MGHuO++q6ASVqsCTSY0Ig0zONhDsvb/HIll43o6yhl2G9r5tm3kKoyb5S6QNhFK6Pf/\n79mbI5wJ5+dK+8e9F/LeY+wO9jeke9vq8RivV847h3OtzkOP+7DpuNnt53Tta+JyzhBz0ah8+gxw\nt+Gqvc5VCCTrtmVh5xjnJNqIjFEigE0xkTYDpzfPmDdeWFCLhu16DUr0b7CzXkqfYaY3vs8Rvt0n\nskz/ps9fJ6UV3odCSNHTxmWNwmlFKMJhn3aaLK01tujPRrH2+HtHfdb+uUzFxx60fa5DVEyvY8zO\nxsoWH1FdrOgEBs24EmuUUhBrNyNMr8pVOCXyA+89KSRUQRXGe6zvOoa249KlSwzB40xFDNC2LVUN\nplCx77t0kRACjdUcHCzJKbAqSdhXL1xi03sOVwIB+a3MEA+PVmxPK+aV/M7QtXgv5JWhnpW0aIvL\nmRAk8DTnyGp1WK5NJBKxKqOshK6OEKP3nllVo51DmSzMOmWIvacPHpNFDJ6DR1nRvjqtiErSPfrN\nGlcZVouGSOTljz3MMPScnd5Ax9s4I4VJ9MVZP2UWTU3IkdNNi8pCxrDaM28s280JOZ7x6le/muef\n/xSbdcvMRYbtGUZpLh42ZfN3tG3HMNzFqsD65CaNMxwtG4kCmllCv+aF45sC04aA95FuuyaGjrOz\nE2Z1w/HJXZYHC9q2ZblccuvWLSrXAIrbN+9werrm2rVreN9zcLQqxZjGOkc9X+BszXwp5/O+f/5h\nvva++3nVK5/k6PAOR6s5KQ0MviPnhLMNIcBydsTNl+7yzDPP8MAD97PtlyzmsuHtL8QhirSKGFks\nD4lBcXoCSWmWiwP6QoZrO2iaOV03sN603L4DhwcXODk+w5klx8ennJzAwcEBm3VPUzu22y3vfveb\n+bEfez//5J98AN+L1MbVC7oh0iwWVHWD0nD/NTF01ronpIxDE1JkvWlx1TVWqxp9GklZc+c0Ex08\n/Ohj+Ds3eOnWTS5fvMTdu3f5hdNf4s6dO7z61U+JC4stqewqoXyAWJx42EOW9p75fZMDWRvGtePT\nR0rWjtKb0TlIXHfS+Doq/Zr7wG/0+KybXtu2fPM3fzO3b9+m73v+2B/7Yzz55JN84zd+IzFGrly5\nwnd913dRVRU/+qM/yg/8wA+gteYd73gHX/M1X/NZT6Dv/QRljVDPvobo3k1ku+kKq83gSkfoB7Ed\nktwzu9u4AFSxAivHmO+llMIVi6ScVMloLdZYIUp3MApyo0CmRu+6kP15YSRPjui7sS0FaFTFlks2\n+GGQTUMy+ZIIsJ3Fx0hQmZmqiD5QIS7oYd1zdtqTzgYOr1wQKM4qhq3Q3Q2Kvt+FN4peb3Q2iTAG\nj+q991dO0Hs/wbTjv2klbMGcMz6GXQq0EcJBHyOm/EwIAafFx9Do3Xy2qsSGbbvdnuv89h1jtIZu\nKzq4s7MzVmVwHss5j0VG3/tJNzd0HU1TY7USssa0KWfazVqifko45mgp1fc9i8UMoxT1rJ4ssC5e\nuQjA+mxLVTWs11tyzpNd2rxZ0HYdi8WK7XbLpQtH5JwJg5cIliyzR6vlU541M/quw5WU65wzd27d\nZrVY0rbi9RkRBvF81pBzYtttUMrgnBHmq1WCZKiEccKebOa1XKuuEwZlFt/CHDObboMxDms1Pnly\nFo9EUxU5AJnKGXHdrxqSF82kVqoYkYvBe86Jlz38CHfu3ib6E2JrsFoJ9dzIOSul6X3L4eoIgMrJ\nTfTQ/Zf55HOf4PjuS3zsl36OF55/FlcZtmdnnJ6eYoyh63pOzloOVhe4dOkKH/zgz/LKJx4khF46\nKSUm78A00zw8vMB6veXo6Ih/8ZM/xe/5PV+BNkuOLlzCaHjw4UfEQq/A9a94xVOcnq6Zz5f8y3/x\nr/jSL/8i5k2NUp6cY7E1NKIhMzUpZQbvmc0WnJ3Ay1/+JF2faRZLvG9BBZkRj7E5RfYyW8x57BUv\nR1tDY+b0fo0pTjm7or1IirJoNx946GXcPfl5DlaH5/SUhxfnhBhZrQ544cWXWBxAs5hT1+Jj2bYt\nly/LGuyK/Obs7IwnnniCC5eO+NF/9E/54R/5KZyF2XxJ3cx5/oUbHB3eRzdA8vIJg2hgTZW5c/uY\n11w4IubEydmaC3rJdtPiI3TAlfuusriy5NatO/zqsx/n6tX7mc1mPPfcp/Ap8qbf9SVsNmdstqeT\n0D7FWNZIgSLHFXBiaetdwWutxtoaYySwdyRxyXXTn+b7igJtlAx5mjk5+k//nv/A47Nueu9///t5\n+umn+SN/5I/w/PPP83Vf93W8/vWv513vehdve9vb+J7v+R7e+9738va3v53v/d7v5b3vfS/OOb76\nq7+ar/iKr+Do6OjXP4HRQzLnicAydWB7c6t9Isn+/8Oum7r3+2GkfJy/SPcSXnI+T+jYj1AZj/2O\ndP8YOxNSLtl45yuXnePGPU74WuMKhk1KMpyNSXxAY/Gu9AnVBVQAkifNB1L2Ig43XTmvfO5a7f9u\nrfU0PxjPNcY4yUPk586zVJVSO1lG3vlntikQeoUaxfDF11J/Rmsqpg34M8XRjOcyumdYa8+ZDFeu\nOefsMT4Mo/XazuF/Z0Q8WlDtV5UGRVXZaYMWd59dlwnivl/lhLZSRWuVySGjHBNsXtcSKNu3HSkF\nCdM0FY2VzYEss1mjDAb5bEFchLSBYExhAKrydYhKk4yYSyejqIzoMLMWu6qqEi9HgyIZiZsiR4w2\nGCWwZUIG/EkjThYxkY0qi4zIgDabjSzWORNVSa5QicEn6tphnEEj5Jqmabh0dJkb6+skJGXc1OI9\nuW3XVJVFm0zjNC/dvA7Av/qXP8FP/Iuf4Kv+y7fy8IP3M5tLaOh8rlgsjmQRzJqULU29BDQvvnid\n3/dfvKWkjxe9ZhLXkrFYqasFs3rOfLHCmIqmmUsnL65laCPm6BlBLgKGqCw+GboAWddk02DRpDwU\nB1LRI2o0PgRS0pJv5yRXES2iQ6cHUGOXEsjKkJMl+ETX9VJ0KINWsbBjRmQqyzB+DJ9GEWNGK4cx\nY9elsZUUpVVVgzH4YtGnFVPBKLFJLUoJ2SoERTec0bYdi8WCwwsHvOlNb0KrD/BzH74jInLjyBgy\nBh+hnouptVISNSTkskBd12glRvchp8k9KltQ1rCslly4cAljDB/7xWe4dOkSFy9fYbNu+dCHf4En\nX/lyrly5yu3bL2HNTJ7RkQehdq45n8k0QZ6BhHVGmJr7a7U63zHfC2NKOgiTDeRv9vism95XfuVX\nTn+/fv069913H//23/5bvv3bvx2AN7/5zXzf930fjz32GJ/3eZ/HaiVGvq9//ev54Ac/yFve8pZf\n9/VVobdmCoMS0d3JxmCmiyadm8K4cX63kw+ILdduliY48iiB4FwkzTg3BPYWyPPMQ+eqPXhtJ94e\n53Zwntmp7pkJQoFo9z7EHVtpryIMnmHdQXCoeY02luwzOip0zJg+U/Ua46EKCd1ELlxe0UWPb2XT\nOzs5xTQVxoonaIgKlcX9wqJFGL93HjnnSacnJ3MeSg5BZmC7TRCi93jfM/jA3edegBBwWWFiZGUq\nhizko3EzGUM892/4Tye4RFTKdF3L4eEhm80WZy22tmIIXq59U4kgeBNLkGQrHdkYLWOMmfLI7n1g\nqqqicXNylg0xl/uJEImM/qkGHxMGcG4m7MAqUzU1bd9xenpKNatohxbXOEzU5GSEaKR29642JYZJ\nC7IgyEEWOrdSk4F1UhmtHTEntKlFXJ8UvaBhxBSoXDOlb0QMpMSQxP0nEcFp2YxCYAiBFPy5Knk/\nrinnLG5GKe+y+mKUuKqsqbLIPsTEu2KxuMzVh57mzt1PSqJHDBidqWcSTeOHxHy15FLRh778sUf5\nu3/vJlevXsVayT1bLi6QYod1Imr3PnPnTkeuBe5br7fy/EYhTmkr10oVQ4TV6pC2C8wPjrCm4fkX\nbqHNArQHAmiZg06pHqoUiTT0wfDSrS1udhnjNEat0al0vkk2yJTKBhQSVjkuX1TkCEMf0QSsK+Qs\nxF0nZ0PCkJPiuRde4uDwIllptHEYKnTRyEpmkUTlaDRKOa4//xJVdYjJcPHwYjEwkKLyytWruKoi\nxMit27epG4Eym0VDP7ScrRNXri5p6oquHwixJQIn69sopfiCL/wdvPzlT/MXvv27+eF/+A/50i//\nHbz5zb+Ln/7pj+BmcPHSBZbLJbZvqYzj9O6ak2O4cuUKdRPx2xYfPGenJ/gon8N8OSP5De3Q8vCj\nj/HMx36VD/3sRzk6POBljz6MD4qf/KmfpqktL3vsIaxNUIhg90Kb5zex0flphC4/XcK1f+/uE2OA\nMqe2RBzq1yi0f6OHyr/BqeA73/lObty4wd/4G3+DP/yH/zD/+l//awA++clP8o3f+I187dd+LT//\n8z/Pt3zLtwDwV//qX+XatWv8/t//+39LJ/jbx28fv3389vHbx28f/7GO3zCR5Qd/8Af56Ec/yp/9\ns3/2121BP9vX7z1++q98+3m/xLwnGbiHiJJzRpudSe/+nGj8vrHi3RFaMkn5XUWR9eTXqMtMUCs7\nfS3njGVHZjmnEWQ3t9o/vwxYN+b/7fsI7kxzx9y0cyJ6IDUOX2n0oqFeLZjpBe2tE2ZeM9zekq+3\nOK9pTI29OOPuQ+LnOJB49e/9fXz4/f8n1I7lwSHGWZp6Lh6f5X2MLubK7LwMS8kl8g776bCEUXli\nYvpUWJlhIAXPyYsv4jdblI/oELi8OGDoeuqZuNrs2Fbn9ZKj6H36HEmYAuMIvOkn/eNyeUDXddN1\nq+taNEI6M3jpcJ3bpUvUdX2OOTpeY1fXxCSv72OgrpoCUyeMszzyu9/GM//4H2OMYbFYsF1vyQpm\n87mEhfq+eEuCsWryXExBDMgrvZMbhORxrp46qZTSZJzd971AwcqKU4yR4GCjK6yVGKlhCGWmzWTN\nlpLEVFWVzCn7rgV22XMj5B5CKL6S95hHa8XZVvR5rppPWrvELrNO0jAyVS3dkCeQrWiiyC3Bb0lx\ng8prUIlh6Fk0M9o+8flf8d/x//zwt/In/9R38he+64+jVKapFMm3hH7DcjWn7zwohzEH9INGuTnv\n+Zbv4L//jj9FjPJ+tIaQ5fNWVkKgu15Rz5aQHd/xF/4yf/7PfRukNYq+MPgisdiiRTI51ygsPir+\nl+//+3z91/1RlE6QjtF4RvZ0ihDGLiLKGvAPfui9vPvd76ZtW4Es0+k09xc3pwWKmpwd73//T/Lq\nz3sNFy4uUHpg8BtmaiHjDTpSHgOoV6i85L0/9GNcvPQw/+hH/xnf/h3fwOqC5tbJ83zdH/8R/sR/\n/Sbe/JbXklLi7/ztH+DG82f8mT/zDSyWgRvP3eK7/ocf4Y1f9jhv/+ovI2XFizcC3/mX/i7/09/+\nBtbbDZoZ7UbznX/pe/nP3/Zm/u+feD8hw9f+gXfy1//KD/JlX/46/sC7fjfNcB0bFNefP+N//Gs/\nyrf8tf8G7U7Bt1Te8is/d5f/+X/9J8wegG/6lj+K606wpqauF3zXf/+9fPxZeNvbXsmTr3wln/rk\nszz44DWamePWrRsslxWP3HeFytlz44fPxMzcP2LYF7OfNxn5NY+9deld3/S+X/v7PsvxWTe9j3zk\nI1y6dIlr167x1FNPEQsFu+s6mqbhxRdf5OrVq1y9enVKCAZ46aWXeN3rXvdZT2CfbQmfLjIfj31r\nsf3vGb9vf6Z3HlY730IrdR7eVEpN+P30O9WObr8/W/pMrKH985asuH3SzE4UP5r1TnCsktidrpAj\nEomowNVOTMSyhgS6MENjDugh4nSNqxuKoYjAsCRmMWKcnd67EEL0JMTfhwvOCdKTOjcnjTFi7B4U\nmzJEYQuGkhmotcyqVBLhbioztXyPWbNSIkMYj/EGTymhciQmSU4fuo66zDtyzjIXQ/IIM5EcIyoL\ng0uXAifB9Pd+TwRvjJG5Y9lMCUjShBLruZghZYXR8v2uBG+KVlOYvH3fk/1ASIF6XjP0Hc7UDKEv\n9N8kol0t7rjTvafFXSiMzFatZC4WJe1ClYgsYxzEyLxeoLWlz0CJ2EohQB4XjFzuA8mnM0qMrMVs\nQeOMxSiLMwmNQRtdrNj8NK+dz+fiQZolDd3aioQml4JMJ8mhSyWiKePIyoCOsilXFSk6hi7Q1Jqq\nrrHGEEbvzRAwFaAMVWVxDjbbDfPlikzGuIq+EwJN2wdMtPQDe+nsWXzjk55GG93QE6nxMUBSdIOw\nhW3yaESnmlVA5QA5Y9CS/WjEczT2W4wSPWcmkXR5JpV8fKrM10vuFfOZQ6uIIqB0FvrZuG6M/2XQ\n2tJuizxEa5R16OzQSchLKRtGza8qsHfbtoR+QCmYz+cY3dFvRS4zn8/FcKIkT1QVEnuWPW0reYyr\n1YoYPdbVZBJVLSkOKUVQokl96KFrvOlNb6Ltj3n/T36ID/zMB/ERrly5DxAYniTSL2vlGg/e01iF\n8bDZbHAOmkYVMqCja3tAo62MLZfLFY+/4glu377Jc89f5+KFAy5fvsrzz32cRmWWizmr1WoiwY3F\n372ypV9r/bwXztyRgso4SUMSZxI+wzL8H3R81k3vZ37mZ3j++ed5z3vew61b4vL9xje+kfe97318\n1Vd9FT/+4z/OG9/4Rl772tfyrd/6rRNj64Mf/OAEdf56x7iZ7ePBY0TJRPoohAKZO1AYcruL5X1b\nIls8Q9+de62MUNzHuZtG4aPIG6pKhOY5iABVAg0tHi8Mx7wf1yMdknW7vDrY/bvE5+QpzXvM0DIl\nPqaqKhaLBW0/EHxAOYdC4ZRhlj3dJuLXHXf1bVTWzIyjntecNKdkFZm7jLOGZXOJ2cGSXGaExio2\n2zUv+Y7F8oD6qsEohQOsipgkA+yEzD28BmUsKCUVspfwIJUzQwh06+J7mRXKam6fHrPebDBWsZjN\nGfqAThqjNfW8IWWNMRUheEI/cHR0hLUVPg5l7hZp2w1oyRE7W5+gUKwWM7abM6xz2GqMiBGN5nrb\nlXujJNsPgyRYqIRzFSTF4L0wTetaNp+kWG/XHFw6om87Lly8JHR3K522q5vymRiUTqReihA/9NSu\nInqPLSSQ+XLByckJOQTmixUh9KhtgiGWCKJaEIUQiBEyicViQd/3nByvWS2XwiZ2FaELNPWMbiuJ\n9wdHh/RtR9000+egjJ2IQVIQUGaj0s31vccZyzBEmnmNMzJz1lacP2IuMpGIaP4QezLrnDjC+FgW\nz8x6vRZPxfmclMEhi/fI6mwqTesHKutIPoAyOL0g6YacFV13itWKdiuG3Q8+/BTv+JqvotKKGI7p\n0sB8aVHJQrK0my3z+YqT7TGrw4scn67RGvo0EFMn3XoQpISUCV1EI6YEKsg8/vQUUIFEJJRn1zmL\nMk6MImISUkoWgs6tOxv6fktVa7LP1IX5KES5hLYNKWpSVAw+8sCDj9INPUPsMAka40hZKPUqS6B1\nzAmrEzduXWexmJFCi1GJ2oDPd4FMUx2iVMN6cwvbBBKBT926jq9nLK8osusxzvLSCy0AD73sMjnA\nnAd54ZmeV3/+JSrbkWzN3bPMkOHqgxdZNAvOtplnXzjjuIW03XJxcZHTTeL0uKdaJppZz1v/szfw\nti/7nfy57/ibrA4h2y3D0HE3aerZATf6gZMIOtzhQBuUV1SV4+OfuotylgceuI84bCC2WGcYkuL6\nDfEgvnLtfrp4yus+/1WcndwlxA2f+NRzvOY1b2Hd3eX29piP/PIv8uVf/DsY+oGqdoU0ZQRtyhHC\nQJUDShuSakoBWhoFJTZlNutiKpJLYofBqrJNRY9W5j+9OP2d73wn73nPe3jXu95F13V827d9G08/\n/TTf9E3fxA/90A/xwAMP8Pa3vx3nHH/6T/9pvv7rvx6lFN/wDd8wkVp+vWPcyfcrgn3Wzz4hQqks\nQZTpHtNnQ3FdkSEyOk+6AWEfFvswn8658A8lxHRMTx47HWudMBzz+Xy6sWUfJQH77bizdXGVN4XM\nJXRngbx6Ysx4H0kxU7zFCSHJwpQdDvGNTClLR2UTGMfiwiFp8OQhsE4dTU5laC+V0dHREfODFc/f\nuE67XbPZzpm7mspa6W6yuBvEnIg5kTBoXcg11uB9lOq4sEhdVoSzDu8HYhYD5zgM+CERfaBRkqOF\nkhDUlBXJD2XDrxid1kNIDMMGW1WoQjLq/YCoR7JYL7kaM7I7yzXVWkuFz3nnBWH1JqwyuLrGloy0\nmIRoY61FW0dK0PuAbnuMMWJsq8dudldkjUfTNJOMwpCnrDXYy9orBJimmY9nQ84lD0wlUlKEmMlo\njHFCcEhMuYdjnhsUU+1SOIWSGhFGM4CSBCKxMOdt2LQWjZlApBLdk4MmayF1GS0Lu/SSCT3mC2ZL\npcRWzpcMNnG6kQ4+5owuSERSSggJKReNjSw6GSCLrtAsG3IOlMhJUlQcrC6KJ6P2JOVJMaJyVQwn\nLAkFJqFUJGWx0JLnyO6eHyNjB50SORl01mhjhMGshJ1qsnRkKefyvEPMMlZg1CWWm0Zg+5F1LcWM\nKgSThCIncabpu6EwHA2mclglCIbOhlzSXco7ZWQy5xzLEyySJe3itIDrPI4KIkPu6IaeNvToWu6V\nGGF91pX7S+6X3DsssJg3uAq2KdMNGTSSuJ6FdbrdBtDQVDW2OEt5H6lmhhBbkm9ZNge88vFH+cBH\nP8GHf+GDvOyhQ17x+P1s+si2SLMuXTxke/cuKoPKij4qAmPmoYwdYnFe6bzC54xxhn44o8mag9Wc\nCFxYH/Hhn/sY9z18iYOjKxhj+NAHP8xrnnpCvEJVpBsCxkpqvMlZNr8sa3hUwkRWSpNJk12kSH52\ne8GY3Cmwf/5Pv+k1TcN3f/d3f9rXv//7v//TvvbWt76Vt771rb+pExm7uv0Z0L2bTUKosWNi8Phv\n1hYT0rzryKYeOAPKCDYfIzEGYdqVqhoFoVgUZSAbjXUWnWXT2/2eHXS6U+Lt5ke1bYgpi+WTNugM\noQibPSLDEKjvPEyqlJJARmSNCT5hFMTgGWLmsG7ICrqY8ENUl7n4AAAgAElEQVQn0R0K4W0jEF+t\nLIeHh4QYOT09JTYzFrM5TVWRVSZqkTb4nMTiLBYLNVWT/UAOEZ2E1mZ8pqoahhBpvafGkI2j9x0+\ndAKtllT3bA2Dj2SVMdbgrGbrW1SQzytk6T5MJbBr20rmnjFj0oUk3gPTnRhJ5PHvKe59/oqQBPqE\nJO8rxUnAvKwb3MxIB2wtPmequiaXDSymXQG1D1VXdTPNWo11k3xCaUPdNGhjpyDeUWKRhDosiRYp\nQYy0vcyksioU+hRlbmekMtUFvgxJJCMhRrTezSgqYyc0g7Sby2l2z8CoBR2fl324eF8asv8+9+eo\nY8E3auHG+bewSovZuNWE4dN1UEopttuO2awGrZk1UtA2zZJXvPyVJH1TbPfLvE00onHaYCdEJEZG\nmdX+3GeEmJUaYWP5fjEg2EHjoxY2xCJxYWcSoaeZ+ficpvIegxQ8xeQ4hpI/BqzXa+bz+XR9rQJd\nDBjSKITOu+stBgJ58tuFMYU9lc1Jrq3SmtAHhiHS9ztINGfptoGp2Oo6CbUeg41zFqmJ0VDXs/K+\nDWfrExGiu53Bwna7ZbVanUPFFssZZ2voPr7hfT/2Y/y3f/KPQjY899wLOCfveSz7Us7iAZzz5FGa\nczF7NhofMtYKzKpUIPjAarkga7h8WfFP/9lP8LKzh3jDF7+2hPk2/Jt/89N83utei6sqlKvoNi1G\nJ5ZNRQyefrsFtxAo2ZmJwZ9IRU+sIUsgbUwins9FmM5eHs9v9vicO7L8Wq4c+xveNJwfOy9jhIZd\nfgZdUtW12GypPblAyqCdxBGhoVL1FBCrUibkRBcl60tZQ1VuRKXUFIi5f8jNH8+d47h4eC+w6PiA\n673Fa1zI9kk3VVVJiK2CZKDWCqsyVBW+Hwh9YOu3mPGhB45PTkhW0yzFhiwl6eCuXr3KZrvl1s2b\nDG1H33YcLJeY2ZI83uJm9CQVCG2IPTokTIIcM/hI2LScnWwIvsengLdq6vgG76mMZTZbYOsaYhZd\nocpYZ8ha0a63aK1ZrIRqLSSOknbegakclZOKMgiGTFbiC5mzhEkqo0tYsMIoRdYS7WScJSrIUaj6\nxsgcS2kr1zBnhhwx1pbNz+CampgTOSaJh9JquubjNQk5FUd3TbYalcRgWNeOkBPZFugtJ3yWPEVt\nDVVToUIkecmns+N9ZeVaSNZjCXItRKKQU6HYJ+q94m50Bdq3sZOv70hHmTzNoa21U8G17yA0PROM\nheR5H1bn3GQEcW5mzq4oOF8YyLOmlWHbbZnNFigyzs7L5Wt4+OHHeOGlU9AVWUUIoktlnLPlOG1u\noqUcocbdvDzGXeAtZYPRWTw0QyjfHwPGSLbaWCxktXNNAkpuJtPfnbWE4o05FqsxRmzR163Xa1YH\ny70N9byp/P6RUmKz2UiBtCdRMsYQynxQKSXEqgQxRLyH7XbLwWpRrmuarNXGtW18j7PZTApj03B2\nusEY+dpYqIif6E4mY4xhvV4zm82m9HilFHfu3EEpeOKJQ577+Akf+9jH+IIv+HKOj48JAc5O1xw2\ns+keEROJyKp4lIpVW6a2sp42DRwcHeLUmqE9E5Qhi73c88+fcvnBDf/uZz7E5z/1OG55gO9a/t3P\nfIhXPPE49z34CHXtIEkotk4yopBnroR97+1i07XXiH65dIUJsUIc57C/leNzvundq3uD86SS/dQF\nlDiSO+0kVDALbNd3A01VF/GpCNIFkhHI04dE54O4KBTChI9BFkKjmS/m02t5H3CkUn3skWvKhQ6D\nJ4Q0VYZNJfqw4AWm0ZTvLVBau5FIF1dgNK3NVKtU2rItVRYlJcA6S7fpqI1Fzx26j6QQMUbcYG7d\nucOLt27SrBZc+6IvIQ0ebTTruyc457j/wiXazZa+7bi9aRnsMaaqmc/nzGYzmqaRfL2UyT6gA2Qf\niF3P9mzN3RdepM6ZWd3Q1JbQ9RiVmVtH0zgW8wWXL11CKcXZySk+JxZNzfr4LrUz1HN58IY40G06\njo6OREidIjEX+LYdxHLK7bqQYa/qR8vyZPYYp0prvAenNBT9nrO1EHn6nnohllRdP0wuKrfvHpcq\n3KNSpjKVJLen4neJ1I2pdGJVJQzUZj4rsU+KruvRzuK7vojtq6lAijnjg2fwAwcHh1jtqOqOpprR\n9QPWVaQgXV1TzcSdZGipq/nU+VslLvIpiTvKYjaXRS3uOpucxXJNWzWJ+cdE99F+7zxjWU1fU8ZO\nxdZ8Pp8g/LZtz0H9YSRaJZlbazPGUuVy31pStITBUNeOTLGXiwsuXzzimWc/QNUUlnBUWFOhdUVO\nknZRVUJ2Ojs7E1gyRiFVjBtW8WEFxHNUKYy1nJycACXaqszYKQUjMLGTk7HEJAv4yYk8s/0wgAlF\nMK6m11ksGqKX93T9+nVe9eSX4n2P0WICPhuJXZwnTXjvefFF2RTqxk1jhnY7EIaMSQOaSNUkhiFw\ndrZls4Hu+i0efujJMmbI3L59B4C6cRgjm1QIkpAeQkAZwwsvvEDTIPZ4Q4tyhjt37tA0ir5v0dbS\nNAtu3HiBJz7/ETabDUdWYNUbN17iwgV493/1B2niwPf8tb/F3/97P05tr+Ks5uDoArHvxZxcZW7f\nvon3iQcfugYqMavFvUfuDVgsoKotsY+sDi/IZ6QTvg2creHatQd5xeMPcTh3PPOpX+GpJ1/D/adr\nbt895iMffZYrV65w6egQV9cQICdxu1Ep0bftHnyvidnI6EaLe07KFNg6IRnIv9U+7/8Dm17+TH+W\nDU5mNjLD0kqcLrQuLhY5oksbrnJhGSJCca2E3TcmJFRVI9iG1rhCilFl7KeVlpkNkHxC5Ty12+xB\nS4rdYjLapo2V1VgVjg/wKNAeQ2rHyhpKCG1x50jsRJYyc0CINSmQ0GjtsJUtJBRQOVJbhwqgCuX3\n+KVbaGOkqm1qDpcrZoUxGPoBPwi043PGFLsgU+zRtDZs2w2hHwitx/sBU1c4pdDW4XPkbLshW42O\niqjggcUSkyAET+o9KiZyiJJPl4SEQC5CfkT+4AchLFTOSGp6CuQUJEdLCaVulC7sz3bv7TrkczLi\neh8RB5yYIYEzFWYu1k21a2hzJ+a4KAyS9yXOI4oU0o6VmzWaLM75SbxFjRNWprGGiKG2NT4NYiis\npLDJCYIPAt8qSwpF/F3+DEOEmcIqwxATOmuJahoilYIQUoGAE1Zp6Q6jdO0ksalD7SDAsYMYglS9\nI4yqjMYWL9L9+xCYjND3mcjj641fG7tGHcPEpBxF3+MmKs+BzERPT89YrVaS1I50epKqLt6fSpky\n7xx/Xk8d3+CL80ipbfbf22iIrpQixd0mNbJ/xyJTmKZ7cGlxNdJak3Ka4FPnHNFbjM7FwCORy79r\nO14LieyZz+ccn7ToYjmWKN3UfjpLec67bjeKmZi7SknYsnJCJgoJ7+MEzRJhsVhIdz4Ezs7OAIFF\nybDZnhETLJcSt5SUKl2doaoaYrvGFljSOpkRq+L+0rYtq9UK6zTWGPq+ZxggFAj54OCAL/6dT/CD\nP/wMh8uXqCrLtu2pjGaszruhJ2VYLGaonLDWoQuZ0DlF3exCq6umpt1sxVC92wp6VlVcvnKR2G54\n6KFH+KVf/BhX77/GbLHi7O4ZL964xdB7rl29gLWy5kxxT0qVfL5MSHmHbCDInba6oAajgcln31M+\n2/E53/SmCpOdfde08GVR4icky00ZTeVkrjLaYY2GwJSBvGxQoItJqc4S0aNKCkEKsiGRxXZJKUX2\n0s6rUm3D+RmDwB67xVdr2fBGHVaMkTEZfZyd9H4ANHU9Ezi2QDK9H6ZNMO5DSmXDHIIwPUe4VBcD\nJRm5ZWI/YLWmMUJcGNZbtDEMw4DftOheYLaUErGYEqes0D7iYwth1w3MZws27VZmCj5jlGJ2tMJk\ncb/JfqBezmlmM+pZRdZKOlsfGNqW0PWiW8uwmi9oN+spESPFyMHBgSzkQTR4TdMIrIGaoDZbQnST\nl03P1hXdZis3vNYiXVAKneFodSTkj0FMlSsn3a8xhqHtmM1mdOV7Qwg0TcOsqhhiIKuIQRFTJvmA\n92UxLcWS1pB8YOh6HJqYE7WrhFVprFiYxSQFC2WykDJOG5SxGAwahc27P2tdyUY+aGonkTc2CzEm\n6QTWlKBckbF4yiwp7yyc0DLjUkbjatH37Zt6j5vcuOHtb3rSqQ7TXC2EOP0u2BUV1tpyf8n7i3GY\nFnSgpNdDU885O21JUeOjzEq324EQM5WbUzlFVlkKogKBWbNDcjKxbDY7mdH+LB924cYxyfMorvxy\nbsYZYctmoYJJ1yrXo6l3cF3OZdNzjsoojE1T1FQIHp09KQk8PAzDpC/bH6Wc0zzukeK8Py+lApnN\nZaWxuSIMcTJHTmlHL1gsFpIJGQa6riRYaEUcPF3XESPTnDUrNRVF1lr6orvdbrdCvGscuRcOgsQ+\nyeda1zWnmzXWwnImJDfte77sS76UF1445tlnOz7x/BnL5YEEAiNFXwhpumZMLjRq0prWtaxfthT5\n3SDG4NvOYyu4/4H7xBsWeZ9HFy9x984JCcOly/cTQqAbtnzqk9d57IEjji5clA2TLPc2mkwssGa5\ntjqVNVfWfjDE1P/HaPQ+95teLBWhLpBgSolq7ISyMHdmS8HDiUkewIhomZQWAkUhpYw4v1FWIJpx\nTrQnNleIzyGALQ78wZ9fRCKisdJqpw1MWXz5QoporRiCn1iGaEVdN3Rdx6bkeIktlphaoxVtL7Em\ni+VKZjveTzdxYkfeyZR5ViqpwcYSfUA5TdPMqOwSV1eTTqrJmqEdMDESCUTjgJ1/Znu6Aa2pmhmr\ngwMurlZ0IdINPT4M1Is5uqkmwkS7bic9k55ZDswS5xzzppLNuBvYbjYM2xaXFat6TvA9+Ezjmsko\nOmu55pszEXxba9GIN6W1hsYKSzLHjDWWaj6TRaj3EpVkDYvlApWh9xI5VLuK3A9U9YymnhFConIV\nKkrHNXSexjWkkLl4cJGUQ+n05DpWVUU39KANy5nMpDQlEzFLF964ihxlZrM+OSVHmcE0Rc+Xy1gB\nAFUkLykxDB3O1fgh4PvA0cEFNEbCfdEMnTBMV4sDIZ5oRzCgkmimxu5lDNBtGmEUy+IZmS8Fvo1k\nSTZ3Fqt3AvWqqbl79y5Xrlzh+vXr1HXNYrGgqmSzdU7mKn3fF3iuoe/76T9TuUJkscQskbXOuomt\nrLXm9u3bkyfj2IHNZwsyA1pXKBWE0TtYYlY464gpMJ83bMMpy+WSk5MTUpIFfhi2DMNA0zQ4rYlR\nnmFrGlxV0YXAnTt3qGvZkLwfyiIoJBVgQl3atsVYx61bt+g6MQPXWhdTdWGAGiM0qCEEKjfD+8Dt\n27c5Pi4weOim19Rai7dnVqCEaXrz5vPMZjJn6/sOVymssfT9BqLFVtJBLlZzjK1of/UmxsDxCVy6\ndJkXX7zJtSsXaUWxIFBmyrz00g2aRl7XOUdWmjt31ly5cgiU3xcy63XHy17+EG3bsnCH+By5e3yb\no6OjMivsylwQ5oc1WmX6dsPhasEf+kN/iG/+5r/OwQpevHmLxmruu3qZ49vH5AyLuXToVdWwOVuT\nSgHV9wMXLz9I5ztM7slJvGiHwRPLeM05x2xW49sNzWzGxYsXWS0zp9uOW8cnXLp0CVM5bnzyV1jU\nidm8JmVJrFkdCImobTekmAh6DBtIGO1wtp7m1hhDSjsTit/s8Tnf9JLVk/7OGINWlq5UvyJNTuTo\n0VmTY6JRDpJU/2qkjaOJodCGQyangA/FQV4x+R6qcZBdev8w7MOLetrkohXoKLJLDp+Sz51FjYrN\nvRnknbMTgWDmzQRzagxDCuKE39To5BhyhCjMQ9c4Rk9RjSZp2RBySCircNpRWYf3xVdRGaySTsEW\ncfXRfEmIkbquJ6gs50xwMuupey8zq6xQw4AKieVixupwSRcii0Y0X0PZ9POipmma0r1mVvNF0U+1\ntG0nwbGj0TOauqokmNOI8Fmur2QWGme5eOV+JGRTyBzZ90IZz5LJN258iYy1DuMs9z0oWV6+JGfo\nco9s+h7v45TGETLiS+kDs2aBj5HsheElVXJFJJKSFrGw0rhmxmymJ/ZrSJmu67ElXaKyhtAPZC3z\nNau0sGVjkoG/EiPprBXaVehJQpSwxk0bYUyZOHgq5wgxlrxBkRKYMqPUTuMa8RD1wyCDe6uprHhy\nysNd7lkFm64VdxXn6LrdAj12bnVdT8XTWASOxJixa9mXAI0/r7We5nsqeCmmzdjpFLq/UlhTo5XB\naMt85srvnJExLOeX8CkRfIdSUmBYY9F5RzBRDJydnTGb7c57YsOaPcapFpmE1kI0OTiQ96aHgFK6\naLuKg0d5fpumYfCBk5MTLlyQZ7JrW2q7R9YpzzjaFW/MNG2OxmpytgKzhSBMWaMn0k3MmePj4+la\nCESsyIhrjtJGiHYYiasKkevXbzCfwVkPV69eZbls2G470R0CImgXxvXBAWXxb1mXgvXq1fsx2pGy\n4u7du4QA83lT7gsZIbRti5hg5JLdGQkBDg5W5BxxVpHTwM1bL4gONcNf/M7/jS//4gf4ojd8IS97\n9FGChyvXrnD1yjU2608xjxFjHT5nQoCLFy8WuDjRx9ELM3LjpTs0c/Gcjakj49HWMVssaGaWNhzz\nvh//cb7gDa/l8pUjji5foY8d/+7DH6Gp59x///10XtO2G4zVJSKsEJSC6BxT2GWpZrVztPqtHJ/z\nTW9+sKLv+93sTCmU2812vPcTiUVpRUoK3C6TDmPIVhOT/FvWRqDKkU0CMhspeg9dGFZJgUFPEKOk\nIRc4zSoCEVVcwOV72dmXKUXShVmYcxGt72jEMY2Gw6LfM07SBkJO+CHIBmE0KUYhVOjCYswZo6QK\nDlFghkAmMObeCWsw292Gq7S4RwxFtJvKwpGtRmfNspEOSmBFuHt6ghlalDW42Zxa1fLahalqKicb\ndxZsOSoEguk7fN9hYyJp+YzQhmQ1SVkSiVyGNUFFYV5axyZ4cpZoJg34wtRThZEWtQWSBLBqUZmd\ndQOxdNFm1ERqS1KKoCPKqCJ5kKIpJc2AXPMRilIql2SCXGZg4pivlKLPARUVK8BrMSCOlBSDBDhT\n7MO6aZPRlSUOJQB4nEMV1jBofOjFpcXY4hohJ9LHSCKJ0bTKYvGGwEkhJayGqPJkNpBzRjs7yWnG\n+a/ShmY+m4x9x01qMpUuX9tut1NnJ7DvMM2Z/R7reD8LUeDRnUPRxJDWQrLZ3ygnizc3Bj1HUJrl\n8pC7p3cIPqGzhAvHGEV/VYhfOcsMytqd9GP8U9txJqgIvkebCmUq1us1TVO68PGeVzvD4gmGRN7b\n2dkZTcME8+Yi3AeBznKSOaBRArVut1spus3OlWh83VQYpbmwxrfbLc7t5B9KJ2Lq0U7e8wj5jtf2\n+PgYI5MXZs0Cawzd5oQxFzcmjzWG09OTSS9qTOL09inDAIv5kmHwmKw4OzsjJUp4dCpQq6fvh6nz\nzUXRFiOTjMEqSDnSbVustVy6dIHHXmX4wAdeYOh+iuhlDfFDFCs84zBGnseQJNPu4OBguibKaIx2\nhKw4PVtT1zPqxqFNJKndXDkp2PYdL9yAz4uBFGEgceFoycHRIb/40WfYDomnn36SZu5Yb44J3jNr\nnGzg42cbBXUYf/e90PJv5vicb3oXrl6eNr2x0vN7tlLDMEzsTZUyQ+dFVFt2+0BiiBKUOV6ce/OW\nbFnEYpnfOCfsT7R0AKgdLT6kVEJIcwHkKVZeOz1QLhtwLpZDALaupt+dKPNENKqSeYlsOrIAa6VQ\n1pZwT5FUGBQ+RYxz1LYWyC5EIsgmByhjSNoQ9G5uMyCLo7WlSo0CvwqbJ5N1IqqEj7IJ9NtTcieU\neutOmbUHoh0rN2tBjgXvJ3PW9fh+IA09OSbmxmFmFblxKKUJ2hCGTPQRW2j/gxZIyFaWthNIyjhh\nHCqvUcVbMlsjszAj4mopbhJDH3DVTGZNEVAJcev34C2qqmRRjmJLpgdH1gajzjv1CLyJxMbA1CEI\n69FzH5BmboK+t0RULp6ZCCEh6MRAEBGvHWc0Y0FVGJZJo5yhh2lONM5aRsYkgFVKOs6UJBy2sqRS\nrOgyUxvv/XEzMwWfCCliq4ocIz4GYVeW3+djwGY36QOrItPYbrfkkCd5wijLGOdm+wv8mGumkqaq\nZ4jDwj0xXioL/Z80WdXFIlmZNQtu34VhSDhdInlSEOp5jEhYROLs7OycJGA8l5R27F3JaXQTTX8+\nl25Y5wGlBAoetbqxdJJGW3LebTTDMEAWRCXEgXFTSBFijjhjGIbA6al8XiiDzx5ikAI5SoCvIBRC\nhLt7966QwrwnhgBKpDw6JaxS4HusdlJcGcvdu3dlMzNi2WjM+J5lXfLeM5s1nJycTNBmVZU4qCAb\n15hZOdqSHRwcTOtQP3QMw9itJ2Fll8t9cHCAIqFVFnu6FEgpslwd8ra3/R42Zz/ML//ybdan/5y6\nnnNweAmyResK5xIYy+Bllnt4uCrFjaw7lavR2XK6PiuM8BpjtqjKYtBYbQlBnteqgle96ikODua8\ndP2TnJyd8sgjj/Dwy57gxRdv8qufuM6DD1zl4PASftiKFVwuAbUZFDJWGgtJo3bGHL/Z43O+6bVD\nL5oTtdMyNW6Gqyq0UthB6O2jGHR+YKdB/qTVGSsvJbqmtGdgrZUiJIia0gEKS1LIelkMd1FyIbJo\n8HQUPH/sfjKFVVnmVFkpefD3ZoVa57IARXkvZa41Wp6BVFQjmSCRqZyd/CqVUmg/zhRLRS2k6Sng\nNhklOjQyriwQQ45EMrZ0sMoZTFVhKzctZmSNziLwNlYTUdKNpkTYy6AbCRt+20mnCFOGoKsrjNLM\nZ3OsLTBUWbTCIJKAMRKqKQvabDZDbTcopagakU3sE38E0rIYszPqlhigKFlvxpQhuzDtvO8n4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YS9j10/HPg41UYtNSAUDshom4mVLCapFqKzxHq+OUPISQ4duZD2dUMbyM6KhQKRKzG4HSGqPM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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "\u003cFigure size 576x396 with 1 Axes\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "get_label_name = metadata.features['label'].int2str\n",
        "\n",
        "for image, label in raw_train.take(2):\n",
        "  plt.figure()\n",
        "  plt.imshow(image)\n",
        "  plt.title(get_label_name(label))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "wvidPx6jeFzf"
      },
      "source": [
        "### Format the Data\n",
        "\n",
        "Use the `tf.image` module to format the images for the task.\n",
        "\n",
        "Resize the images to a fixes input size, and rescale the input channels to a range of `[-1,1]`\n",
        "\n",
        "\u003c!-- TODO(markdaoust): fix the keras_applications preprocessing functions to work in tf2 --\u003e"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "y3PM6GVHcC31"
      },
      "outputs": [],
      "source": [
        "IMG_SIZE = 160 # All images will be resized to 160x160\n",
        "\n",
        "def format_example(image, label):\n",
        "  image = tf.cast(image, tf.float32)\n",
        "  image = (image/127.5) - 1\n",
        "  image = tf.image.resize(image, (IMG_SIZE, IMG_SIZE))\n",
        "  return image, label"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "i2MRh_AeBtOM"
      },
      "source": [
        "Apply this function to each item in the dataset using the map method:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "SFZ6ZW7KSXP9"
      },
      "outputs": [],
      "source": [
        "train = raw_train.map(format_example)\n",
        "validation = raw_validation.map(format_example)\n",
        "test = raw_test.map(format_example)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "E5ifgXDuBfOC"
      },
      "source": [
        "Now shuffle and batch the data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "Yic-I66m6Isv"
      },
      "outputs": [],
      "source": [
        "BATCH_SIZE = 32\n",
        "SHUFFLE_BUFFER_SIZE = 1000"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "p3UUPdm86LNC"
      },
      "outputs": [],
      "source": [
        "train_batches = train.shuffle(SHUFFLE_BUFFER_SIZE).batch(BATCH_SIZE)\n",
        "validation_batches = validation.batch(BATCH_SIZE)\n",
        "test_batches = test.batch(BATCH_SIZE)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "02rJpcFtChP0"
      },
      "source": [
        "Inspect a batch of data:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 130247,
          "status": "ok",
          "timestamp": 1551651634390
        },
        "id": "iknFo3ELBVho",
        "outputId": "6daad7cf-5151-44f8-87e8-40ed76cf406f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "TensorShape([32, 160, 160, 3])"
            ]
          },
          "execution_count": 13,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "for image_batch, label_batch in train_batches.take(1):\n",
        "  pass\n",
        "\n",
        "image_batch.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "OkH-kazQecHB"
      },
      "source": [
        "## Create the base model\n",
        "\n",
        "We will create the base model from the **MobileNet V2** model developed at Google, and pre-trained on the ImageNet dataset, a large dataset of 1.4M images and 1000 classes of web images. This is a powerful model. Let's see what the features that it has learned can do for our cat vs. dog problem.\n",
        "\n",
        "First, we need to pick which intermediate layer of MobileNet V2 we will use for feature extraction. A common practice is to use the output of the very last layer before the flatten operation, the so-called \"bottleneck layer\". The reasoning here is that the following fully-connected layers will be too specialized to the task the network was trained on, and thus the features learned by these layers won't be very useful for a new task. The bottleneck features, however, retain much generality.\n",
        "\n",
        "Let's instantiate an MobileNet V2 model pre-loaded with weights trained on ImageNet. By specifying the **include_top=False** argument, we load a network that doesn't include the classification layers at the top, which is ideal for feature extraction."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 136651,
          "status": "ok",
          "timestamp": 1551651640795
        },
        "id": "19IQ2gqneqmS",
        "outputId": "2659862d-e726-47b0-cdd9-355938312718"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading data from https://github.com/JonathanCMitchell/mobilenet_v2_keras/releases/download/v1.1/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_160_no_top.h5\n",
            "9412608/9406464 [==============================] - 1s 0us/step\n"
          ]
        }
      ],
      "source": [
        "IMG_SHAPE = (IMG_SIZE, IMG_SIZE, 3)\n",
        "\n",
        "# Create the base model from the pre-trained model MobileNet V2\n",
        "base_model = tf.keras.applications.MobileNetV2(input_shape=IMG_SHAPE,\n",
        "                                               include_top=False, \n",
        "                                               weights='imagenet')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "AqcsxoJIEVXZ"
      },
      "source": [
        "This feature extractor converts each `160x160x3` image to a `5x5x1280` block of features. See what it does to the example batch of images:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 139095,
          "status": "ok",
          "timestamp": 1551651643240
        },
        "id": "Y-2LJL0EEUcx",
        "outputId": "408397a1-880f-450f-f9c8-bdc39bf6b83a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(32, 5, 5, 1280)\n"
          ]
        }
      ],
      "source": [
        "feature_batch = base_model(image_batch)\n",
        "print(feature_batch.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "rlx56nQtfe8Y"
      },
      "source": [
        "## Feature extraction\n",
        "We will freeze the convolutional base created from the previous step and use that as a feature extractor, add a classifier on top of it and train the top-level classifier."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "CnMLieHBCwil"
      },
      "source": [
        "### Freeze the convolutional base\n",
        "It's important to freeze the convolutional based before we compile and train the model. By freezing (or setting `layer.trainable = False`), we prevent the weights in these layers from being updated during training."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "OTCJH4bphOeo"
      },
      "outputs": [],
      "source": [
        "base_model.trainable = False"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 5593
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 139097,
          "status": "ok",
          "timestamp": 1551651643245
        },
        "id": "KpbzSmPkDa-N",
        "outputId": "a6e319e3-92de-4153-b10a-d7b34d0e4f6d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"mobilenetv2_1.00_160\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            [(None, 160, 160, 3) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "Conv1_pad (ZeroPadding2D)       (None, 161, 161, 3)  0           input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "Conv1 (Conv2D)                  (None, 80, 80, 32)   864         Conv1_pad[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "bn_Conv1 (BatchNormalizationV1) (None, 80, 80, 32)   128         Conv1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "Conv1_relu (ReLU)               (None, 80, 80, 32)   0           bn_Conv1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_depthwise (Depthw (None, 80, 80, 32)   288         Conv1_relu[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_depthwise_BN (Bat (None, 80, 80, 32)   128         expanded_conv_depthwise[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_depthwise_relu (R (None, 80, 80, 32)   0           expanded_conv_depthwise_BN[0][0] \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_project (Conv2D)  (None, 80, 80, 16)   512         expanded_conv_depthwise_relu[0][0\n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_project_BN (Batch (None, 80, 80, 16)   64          expanded_conv_project[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_1_expand (Conv2D)         (None, 80, 80, 96)   1536        expanded_conv_project_BN[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "block_1_expand_BN (BatchNormali (None, 80, 80, 96)   384         block_1_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_1_expand_relu (ReLU)      (None, 80, 80, 96)   0           block_1_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_1_pad (ZeroPadding2D)     (None, 81, 81, 96)   0           block_1_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_1_depthwise (DepthwiseCon (None, 40, 40, 96)   864         block_1_pad[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_1_depthwise_BN (BatchNorm (None, 40, 40, 96)   384         block_1_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_1_depthwise_relu (ReLU)   (None, 40, 40, 96)   0           block_1_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_1_project (Conv2D)        (None, 40, 40, 24)   2304        block_1_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_1_project_BN (BatchNormal (None, 40, 40, 24)   96          block_1_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_2_expand (Conv2D)         (None, 40, 40, 144)  3456        block_1_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_2_expand_BN (BatchNormali (None, 40, 40, 144)  576         block_2_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_2_expand_relu (ReLU)      (None, 40, 40, 144)  0           block_2_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_2_depthwise (DepthwiseCon (None, 40, 40, 144)  1296        block_2_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_2_depthwise_BN (BatchNorm (None, 40, 40, 144)  576         block_2_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_2_depthwise_relu (ReLU)   (None, 40, 40, 144)  0           block_2_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_2_project (Conv2D)        (None, 40, 40, 24)   3456        block_2_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_2_project_BN (BatchNormal (None, 40, 40, 24)   96          block_2_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_2_add (Add)               (None, 40, 40, 24)   0           block_1_project_BN[0][0]         \n",
            "                                                                 block_2_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_3_expand (Conv2D)         (None, 40, 40, 144)  3456        block_2_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_3_expand_BN (BatchNormali (None, 40, 40, 144)  576         block_3_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_3_expand_relu (ReLU)      (None, 40, 40, 144)  0           block_3_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_3_pad (ZeroPadding2D)     (None, 41, 41, 144)  0           block_3_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_3_depthwise (DepthwiseCon (None, 20, 20, 144)  1296        block_3_pad[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_3_depthwise_BN (BatchNorm (None, 20, 20, 144)  576         block_3_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_3_depthwise_relu (ReLU)   (None, 20, 20, 144)  0           block_3_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_3_project (Conv2D)        (None, 20, 20, 32)   4608        block_3_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_3_project_BN (BatchNormal (None, 20, 20, 32)   128         block_3_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_4_expand (Conv2D)         (None, 20, 20, 192)  6144        block_3_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_4_expand_BN (BatchNormali (None, 20, 20, 192)  768         block_4_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_4_expand_relu (ReLU)      (None, 20, 20, 192)  0           block_4_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_4_depthwise (DepthwiseCon (None, 20, 20, 192)  1728        block_4_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_4_depthwise_BN (BatchNorm (None, 20, 20, 192)  768         block_4_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_4_depthwise_relu (ReLU)   (None, 20, 20, 192)  0           block_4_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_4_project (Conv2D)        (None, 20, 20, 32)   6144        block_4_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_4_project_BN (BatchNormal (None, 20, 20, 32)   128         block_4_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_4_add (Add)               (None, 20, 20, 32)   0           block_3_project_BN[0][0]         \n",
            "                                                                 block_4_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_5_expand (Conv2D)         (None, 20, 20, 192)  6144        block_4_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_5_expand_BN (BatchNormali (None, 20, 20, 192)  768         block_5_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_5_expand_relu (ReLU)      (None, 20, 20, 192)  0           block_5_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_5_depthwise (DepthwiseCon (None, 20, 20, 192)  1728        block_5_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_5_depthwise_BN (BatchNorm (None, 20, 20, 192)  768         block_5_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_5_depthwise_relu (ReLU)   (None, 20, 20, 192)  0           block_5_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_5_project (Conv2D)        (None, 20, 20, 32)   6144        block_5_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_5_project_BN (BatchNormal (None, 20, 20, 32)   128         block_5_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_5_add (Add)               (None, 20, 20, 32)   0           block_4_add[0][0]                \n",
            "                                                                 block_5_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_6_expand (Conv2D)         (None, 20, 20, 192)  6144        block_5_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_6_expand_BN (BatchNormali (None, 20, 20, 192)  768         block_6_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_6_expand_relu (ReLU)      (None, 20, 20, 192)  0           block_6_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_6_pad (ZeroPadding2D)     (None, 21, 21, 192)  0           block_6_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_6_depthwise (DepthwiseCon (None, 10, 10, 192)  1728        block_6_pad[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_6_depthwise_BN (BatchNorm (None, 10, 10, 192)  768         block_6_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_6_depthwise_relu (ReLU)   (None, 10, 10, 192)  0           block_6_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_6_project (Conv2D)        (None, 10, 10, 64)   12288       block_6_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_6_project_BN (BatchNormal (None, 10, 10, 64)   256         block_6_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_7_expand (Conv2D)         (None, 10, 10, 384)  24576       block_6_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_7_expand_BN (BatchNormali (None, 10, 10, 384)  1536        block_7_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_7_expand_relu (ReLU)      (None, 10, 10, 384)  0           block_7_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_7_depthwise (DepthwiseCon (None, 10, 10, 384)  3456        block_7_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_7_depthwise_BN (BatchNorm (None, 10, 10, 384)  1536        block_7_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_7_depthwise_relu (ReLU)   (None, 10, 10, 384)  0           block_7_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_7_project (Conv2D)        (None, 10, 10, 64)   24576       block_7_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_7_project_BN (BatchNormal (None, 10, 10, 64)   256         block_7_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_7_add (Add)               (None, 10, 10, 64)   0           block_6_project_BN[0][0]         \n",
            "                                                                 block_7_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_8_expand (Conv2D)         (None, 10, 10, 384)  24576       block_7_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_8_expand_BN (BatchNormali (None, 10, 10, 384)  1536        block_8_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_8_expand_relu (ReLU)      (None, 10, 10, 384)  0           block_8_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_8_depthwise (DepthwiseCon (None, 10, 10, 384)  3456        block_8_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_8_depthwise_BN (BatchNorm (None, 10, 10, 384)  1536        block_8_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_8_depthwise_relu (ReLU)   (None, 10, 10, 384)  0           block_8_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_8_project (Conv2D)        (None, 10, 10, 64)   24576       block_8_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_8_project_BN (BatchNormal (None, 10, 10, 64)   256         block_8_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_8_add (Add)               (None, 10, 10, 64)   0           block_7_add[0][0]                \n",
            "                                                                 block_8_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_9_expand (Conv2D)         (None, 10, 10, 384)  24576       block_8_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_9_expand_BN (BatchNormali (None, 10, 10, 384)  1536        block_9_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_9_expand_relu (ReLU)      (None, 10, 10, 384)  0           block_9_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_9_depthwise (DepthwiseCon (None, 10, 10, 384)  3456        block_9_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_9_depthwise_BN (BatchNorm (None, 10, 10, 384)  1536        block_9_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_9_depthwise_relu (ReLU)   (None, 10, 10, 384)  0           block_9_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_9_project (Conv2D)        (None, 10, 10, 64)   24576       block_9_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_9_project_BN (BatchNormal (None, 10, 10, 64)   256         block_9_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_9_add (Add)               (None, 10, 10, 64)   0           block_8_add[0][0]                \n",
            "                                                                 block_9_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_10_expand (Conv2D)        (None, 10, 10, 384)  24576       block_9_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_10_expand_BN (BatchNormal (None, 10, 10, 384)  1536        block_10_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_10_expand_relu (ReLU)     (None, 10, 10, 384)  0           block_10_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_10_depthwise (DepthwiseCo (None, 10, 10, 384)  3456        block_10_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_10_depthwise_BN (BatchNor (None, 10, 10, 384)  1536        block_10_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_10_depthwise_relu (ReLU)  (None, 10, 10, 384)  0           block_10_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_10_project (Conv2D)       (None, 10, 10, 96)   36864       block_10_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_10_project_BN (BatchNorma (None, 10, 10, 96)   384         block_10_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_11_expand (Conv2D)        (None, 10, 10, 576)  55296       block_10_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_11_expand_BN (BatchNormal (None, 10, 10, 576)  2304        block_11_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_11_expand_relu (ReLU)     (None, 10, 10, 576)  0           block_11_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_11_depthwise (DepthwiseCo (None, 10, 10, 576)  5184        block_11_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_11_depthwise_BN (BatchNor (None, 10, 10, 576)  2304        block_11_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_11_depthwise_relu (ReLU)  (None, 10, 10, 576)  0           block_11_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_11_project (Conv2D)       (None, 10, 10, 96)   55296       block_11_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_11_project_BN (BatchNorma (None, 10, 10, 96)   384         block_11_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_11_add (Add)              (None, 10, 10, 96)   0           block_10_project_BN[0][0]        \n",
            "                                                                 block_11_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_12_expand (Conv2D)        (None, 10, 10, 576)  55296       block_11_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_12_expand_BN (BatchNormal (None, 10, 10, 576)  2304        block_12_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_12_expand_relu (ReLU)     (None, 10, 10, 576)  0           block_12_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_12_depthwise (DepthwiseCo (None, 10, 10, 576)  5184        block_12_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_12_depthwise_BN (BatchNor (None, 10, 10, 576)  2304        block_12_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_12_depthwise_relu (ReLU)  (None, 10, 10, 576)  0           block_12_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_12_project (Conv2D)       (None, 10, 10, 96)   55296       block_12_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_12_project_BN (BatchNorma (None, 10, 10, 96)   384         block_12_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_12_add (Add)              (None, 10, 10, 96)   0           block_11_add[0][0]               \n",
            "                                                                 block_12_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_13_expand (Conv2D)        (None, 10, 10, 576)  55296       block_12_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_13_expand_BN (BatchNormal (None, 10, 10, 576)  2304        block_13_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_13_expand_relu (ReLU)     (None, 10, 10, 576)  0           block_13_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_13_pad (ZeroPadding2D)    (None, 11, 11, 576)  0           block_13_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_13_depthwise (DepthwiseCo (None, 5, 5, 576)    5184        block_13_pad[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_13_depthwise_BN (BatchNor (None, 5, 5, 576)    2304        block_13_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_13_depthwise_relu (ReLU)  (None, 5, 5, 576)    0           block_13_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_13_project (Conv2D)       (None, 5, 5, 160)    92160       block_13_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_13_project_BN (BatchNorma (None, 5, 5, 160)    640         block_13_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_14_expand (Conv2D)        (None, 5, 5, 960)    153600      block_13_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_14_expand_BN (BatchNormal (None, 5, 5, 960)    3840        block_14_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_14_expand_relu (ReLU)     (None, 5, 5, 960)    0           block_14_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_14_depthwise (DepthwiseCo (None, 5, 5, 960)    8640        block_14_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_14_depthwise_BN (BatchNor (None, 5, 5, 960)    3840        block_14_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_14_depthwise_relu (ReLU)  (None, 5, 5, 960)    0           block_14_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_14_project (Conv2D)       (None, 5, 5, 160)    153600      block_14_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_14_project_BN (BatchNorma (None, 5, 5, 160)    640         block_14_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_14_add (Add)              (None, 5, 5, 160)    0           block_13_project_BN[0][0]        \n",
            "                                                                 block_14_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_15_expand (Conv2D)        (None, 5, 5, 960)    153600      block_14_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_15_expand_BN (BatchNormal (None, 5, 5, 960)    3840        block_15_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_15_expand_relu (ReLU)     (None, 5, 5, 960)    0           block_15_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_15_depthwise (DepthwiseCo (None, 5, 5, 960)    8640        block_15_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_15_depthwise_BN (BatchNor (None, 5, 5, 960)    3840        block_15_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_15_depthwise_relu (ReLU)  (None, 5, 5, 960)    0           block_15_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_15_project (Conv2D)       (None, 5, 5, 160)    153600      block_15_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_15_project_BN (BatchNorma (None, 5, 5, 160)    640         block_15_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_15_add (Add)              (None, 5, 5, 160)    0           block_14_add[0][0]               \n",
            "                                                                 block_15_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_16_expand (Conv2D)        (None, 5, 5, 960)    153600      block_15_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_16_expand_BN (BatchNormal (None, 5, 5, 960)    3840        block_16_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_16_expand_relu (ReLU)     (None, 5, 5, 960)    0           block_16_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_16_depthwise (DepthwiseCo (None, 5, 5, 960)    8640        block_16_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_16_depthwise_BN (BatchNor (None, 5, 5, 960)    3840        block_16_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_16_depthwise_relu (ReLU)  (None, 5, 5, 960)    0           block_16_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_16_project (Conv2D)       (None, 5, 5, 320)    307200      block_16_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_16_project_BN (BatchNorma (None, 5, 5, 320)    1280        block_16_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "Conv_1 (Conv2D)                 (None, 5, 5, 1280)   409600      block_16_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "Conv_1_bn (BatchNormalizationV1 (None, 5, 5, 1280)   5120        Conv_1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "out_relu (ReLU)                 (None, 5, 5, 1280)   0           Conv_1_bn[0][0]                  \n",
            "==================================================================================================\n",
            "Total params: 2,257,984\n",
            "Trainable params: 0\n",
            "Non-trainable params: 2,257,984\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# Let's take a look at the base model architecture\n",
        "base_model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "wdMRM8YModbk"
      },
      "source": [
        "### Add a classification head"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "QBc31c4tMOdH"
      },
      "source": [
        "To generate predictions from the block of features, average over the spatial `5x5` spatial locations, using a `tf.keras.layers.GlobalAveragePlloing2d` layer to convert the features to  a single 1280-element vector per image."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 139098,
          "status": "ok",
          "timestamp": 1551651643247
        },
        "id": "dLnpMF5KOALm",
        "outputId": "652a7b64-d9a4-4bf8-936c-f75d74afaa35"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(32, 1280)\n"
          ]
        }
      ],
      "source": [
        "global_average_layer = tf.keras.layers.GlobalAveragePooling2D()\n",
        "feature_batch_average = global_average_layer(feature_batch)\n",
        "print(feature_batch_average.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "O1p0OJBR6dOT"
      },
      "source": [
        "On top of apply a `tf.keras.layers.Dense` layer to convert these features into a single prediction per image. Don't use an activation function here, because this prediction will be treated as a `logit`: positive numbers predict class 1, negative numbers predict class 0."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 139098,
          "status": "ok",
          "timestamp": 1551651643248
        },
        "id": "Wv4afXKj6cVa",
        "outputId": "aa2cd81f-016e-4650-deba-10b48c36e727"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(32, 1)\n"
          ]
        }
      ],
      "source": [
        "prediction_layer = keras.layers.Dense(1)\n",
        "prediction_batch = prediction_layer(feature_batch_average)\n",
        "print(prediction_batch.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "0iqnBeZrfoIc"
      },
      "source": [
        "Now stack the feature extractor, and these two layers using a `tf.keras.Sequential` model:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "eApvroIyn1K0"
      },
      "outputs": [],
      "source": [
        "model = tf.keras.Sequential([\n",
        "  base_model,\n",
        "  global_average_layer,\n",
        "  prediction_layer\n",
        "])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "g0ylJXE_kRLi"
      },
      "source": [
        "### Compile the model\n",
        "\n",
        "You must compile the model before training it. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "RpR8HdyMhukJ"
      },
      "outputs": [],
      "source": [
        "base_learning_rate = 0.0001\n",
        "model.compile(optimizer=tf.keras.optimizers.RMSprop(lr=base_learning_rate), \n",
        "              loss='binary_crossentropy', \n",
        "              metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 142987,
          "status": "ok",
          "timestamp": 1551651647140
        },
        "id": "I8ARiyMFsgbH",
        "outputId": "b526ba94-551e-47c8-bf37-22c35b0d1c5a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "mobilenetv2_1.00_160 (Model) (None, 5, 5, 1280)        2257984   \n",
            "_________________________________________________________________\n",
            "global_average_pooling2d (Gl (None, 1280)              0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 1)                 1281      \n",
            "=================================================================\n",
            "Total params: 2,259,265\n",
            "Trainable params: 1,281\n",
            "Non-trainable params: 2,257,984\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "lxOcmVr0ydFZ"
      },
      "source": [
        "These 1.2K trainable parameters are divided among 2 `tf.Variable` objects, the weights and biases of the two dense layers: "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 142986,
          "status": "ok",
          "timestamp": 1551651647141
        },
        "id": "krvBumovycVA",
        "outputId": "cb122b3e-f832-4bd6-c47d-19c18e92ed9b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "execution_count": 23,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(model.trainable_variables)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "RxvgOYTDSWTx"
      },
      "source": [
        "### Train the model\n",
        "\n",
        "After training for 10 epochs, we are able to get ~96% accuracy. \n",
        "\n",
        "\u003c!-- TODO(markdaoust): delete steps_per_epoch in TensorFlow r1.14/r2.0 --\u003e"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "hlHEavK7DUI7"
      },
      "outputs": [],
      "source": [
        "num_train, num_val, num_test = (\n",
        "  metadata.splits['train'].num_examples*weight/10\n",
        "  for weight in SPLIT_WEIGHTS\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 147261,
          "status": "ok",
          "timestamp": 1551651651418
        },
        "id": "Om4O3EESkab1",
        "outputId": "77b1af8a-4caf-4fcc-8d92-609ddabc6f29"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "20/20 [==============================] - 4s 219ms/step - loss: 3.1885 - accuracy: 0.6109\n"
          ]
        }
      ],
      "source": [
        "initial_epochs = 10\n",
        "steps_per_epoch = round(num_train)//BATCH_SIZE\n",
        "validation_steps = 20\n",
        "\n",
        "loss0,accuracy0 = model.evaluate(validation_batches, steps = validation_steps)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 147262,
          "status": "ok",
          "timestamp": 1551651651420
        },
        "id": "8cYT1c48CuSd",
        "outputId": "8026556e-51c9-4173-c5de-e4e0df821e31"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "initial loss: 3.19\n",
            "initial accuracy: 0.61\n"
          ]
        }
      ],
      "source": [
        "print(\"initial loss: {:.2f}\".format(loss0))\n",
        "print(\"initial accuracy: {:.2f}\".format(accuracy0))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 1112885,
          "status": "ok",
          "timestamp": 1551652617045
        },
        "id": "JsaRFlZ9B6WK",
        "outputId": "9d5c9af0-c756-410f-e203-68c869be6801"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "581/581 [==============================] - 102s 175ms/step - loss: 1.8917 - accuracy: 0.7606 - val_loss: 0.8860 - val_accuracy: 0.8828\n",
            "Epoch 2/10\n",
            "581/581 [==============================] - 94s 161ms/step - loss: 0.9989 - accuracy: 0.8697 - val_loss: 0.4330 - val_accuracy: 0.9281\n",
            "Epoch 3/10\n",
            "581/581 [==============================] - 99s 170ms/step - loss: 0.7417 - accuracy: 0.8995 - val_loss: 0.2629 - val_accuracy: 0.9469\n",
            "Epoch 4/10\n",
            "581/581 [==============================] - 94s 162ms/step - loss: 0.6681 - accuracy: 0.9133 - val_loss: 0.2753 - val_accuracy: 0.9563\n",
            "Epoch 5/10\n",
            "581/581 [==============================] - 98s 169ms/step - loss: 0.5944 - accuracy: 0.9220 - val_loss: 0.2410 - val_accuracy: 0.9641\n",
            "Epoch 6/10\n",
            "581/581 [==============================] - 98s 169ms/step - loss: 0.5779 - accuracy: 0.9252 - val_loss: 0.2003 - val_accuracy: 0.9656\n",
            "Epoch 7/10\n",
            "581/581 [==============================] - 94s 162ms/step - loss: 0.5151 - accuracy: 0.9328 - val_loss: 0.2115 - val_accuracy: 0.9688\n",
            "Epoch 8/10\n",
            "581/581 [==============================] - 95s 164ms/step - loss: 0.5076 - accuracy: 0.9346 - val_loss: 0.1678 - val_accuracy: 0.9703\n",
            "Epoch 9/10\n",
            "581/581 [==============================] - 96s 165ms/step - loss: 0.4679 - accuracy: 0.9368 - val_loss: 0.1668 - val_accuracy: 0.9703\n",
            "Epoch 10/10\n",
            "581/581 [==============================] - 96s 165ms/step - loss: 0.4921 - accuracy: 0.9381 - val_loss: 0.1847 - val_accuracy: 0.9719\n"
          ]
        }
      ],
      "source": [
        "history = model.fit(train_batches.repeat(),\n",
        "                    epochs=initial_epochs,\n",
        "                    steps_per_epoch = steps_per_epoch,\n",
        "                    validation_data=validation_batches.repeat(), \n",
        "                    validation_steps=validation_steps)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Hd94CKImf8vi"
      },
      "source": [
        "### Learning curves\n",
        "\n",
        "Let's take a look at the learning curves of the training and validation accuracy / loss, when using the MobileNet V2 base model as a fixed feature extractor. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 512
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 1113660,
          "status": "ok",
          "timestamp": 1551652617821
        },
        "id": "53OTCh3jnbwV",
        "outputId": "3e7ed689-c5ad-4cfd-8416-1a02e34ac953"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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SQtQ/EuoOYFWsfHduK9+e3YxOrWNqp0n0Du7p6LKajLxCE6dLA/xkUhYXruSi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\n",
            "text/plain": [
              "\u003cFigure size 576x576 with 2 Axes\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "acc = history.history['accuracy']\n",
        "val_acc = history.history['val_accuracy']\n",
        "\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "plt.figure(figsize=(8, 8))\n",
        "plt.subplot(2, 1, 1)\n",
        "plt.plot(acc, label='Training Accuracy')\n",
        "plt.plot(val_acc, label='Validation Accuracy')\n",
        "plt.legend(loc='lower right')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.ylim([min(plt.ylim()),1])\n",
        "plt.title('Training and Validation Accuracy')\n",
        "\n",
        "plt.subplot(2, 1, 2)\n",
        "plt.plot(loss, label='Training Loss')\n",
        "plt.plot(val_loss, label='Validation Loss')\n",
        "plt.legend(loc='upper right')\n",
        "plt.ylabel('Cross Entropy')\n",
        "plt.ylim([0,1.0])\n",
        "plt.title('Training and Validation Loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "foWMyyUHbc1j"
      },
      "source": [
        "Note: If you are wondering why the validation metrics are clearly better than the training metrics, the main factor is because of layers like `tf.keras.layers.BatchNormalization` and `tf.keras.layers.Dropout` that add randomness making training more dificult. To a lesser extent, it is also because training metrics report the average for an epoch, while validation metrics are evaluated after the epoch, so validation metrics see a model that has trained slightly longer."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "CqwV-CRdS6Nv"
      },
      "source": [
        "## Fine tuning\n",
        "In our feature extraction experiment, we were only training a few layers on top of an MobileNet V2 base model. The weights of the pre-trained network were **not** updated during training. One way to increase performance even further is to \"fine-tune\" the weights of the top layers of the pre-trained model alongside the training of the top-level classifier. The training process will force the weights to be tuned from generic features maps to features associated specifically to our dataset.\n",
        "\n",
        "Note: this should only be attempted after you have trained the top-level classifier with the pre-trained model set to non-trainable. If you add a randomly initialized classifier on top of a pre-trained model and attempt to train all layers jointly, the magnitude of the gradient updates will be too large (due to the random weights from the classifier) and your pre-trained model will just forget everything it has learned.\n",
        "\n",
        "Additionally, the reasoning behind fine-tuning the top layers of the pre-trained model rather than all layers of the pre-trained model is the following: in a convnet, the higher up a layer is, the more specialized it is. The first few layers in a convnet learned very simple and generic features, which generalize to almost all types of images. But as you go higher up, the features are increasingly more specific to the dataset that the model was trained on. The goal of fine-tuning is to adapt these specialized features to work with the new dataset."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "CPXnzUK0QonF"
      },
      "source": [
        "### Un-freeze the top layers of the model\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "rfxv_ifotQak"
      },
      "source": [
        "All we need to do is unfreeze the `base_model`, and set the bottom layers be un-trainable. Then, recompile the model (necessary for these changes to take effect), and resume training. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "4nzcagVitLQm"
      },
      "outputs": [],
      "source": [
        "base_model.trainable = True"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 1113662,
          "status": "ok",
          "timestamp": 1551652617826
        },
        "id": "-4HgVAacRs5v",
        "outputId": "9ce5967c-8d22-45bd-84ff-c34ef57c1ec1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of layers in the base model:  155\n"
          ]
        }
      ],
      "source": [
        "# Let's take a look to see how many layers are in the base model\n",
        "print(\"Number of layers in the base model: \", len(base_model.layers))\n",
        "\n",
        "# Fine tune from this layer onwards\n",
        "fine_tune_at = 100\n",
        "\n",
        "# Freeze all the layers before the `fine_tune_at` layer\n",
        "for layer in base_model.layers[:fine_tune_at]:\n",
        "  layer.trainable =  False"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "4Uk1dgsxT0IS"
      },
      "source": [
        "### Compile the model\n",
        "\n",
        "Compile the model using a much-lower training rate."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "NtUnaz0WUDva"
      },
      "outputs": [],
      "source": [
        "\n",
        "model.compile(loss='binary_crossentropy',\n",
        "              optimizer = tf.keras.optimizers.RMSprop(lr=base_learning_rate/10),\n",
        "              metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 1113662,
          "status": "ok",
          "timestamp": 1551652617829
        },
        "id": "WwBWy7J2kZvA",
        "outputId": "d7bab642-9e06-4790-aacb-20e6924079a3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "mobilenetv2_1.00_160 (Model) (None, 5, 5, 1280)        2257984   \n",
            "_________________________________________________________________\n",
            "global_average_pooling2d (Gl (None, 1280)              0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 1)                 1281      \n",
            "=================================================================\n",
            "Total params: 2,259,265\n",
            "Trainable params: 1,863,873\n",
            "Non-trainable params: 395,392\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 1113662,
          "status": "ok",
          "timestamp": 1551652617830
        },
        "id": "bNXelbMQtonr",
        "outputId": "1d7df962-139b-4e25-8367-4533680b79ce"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "58"
            ]
          },
          "execution_count": 33,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(model.trainable_variables)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "4G5O4jd6TuAG"
      },
      "source": [
        "### Continue Train the model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "0foWUN-yDLo_"
      },
      "source": [
        "If you trained to convergence earlier, this will get you a few percent more accuracy."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 2288350,
          "status": "ok",
          "timestamp": 1551653792519
        },
        "id": "ECQLkAsFTlun",
        "outputId": "4532df99-0c58-4321-baeb-7b6a2432c4fa"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 11/20\n",
            "581/581 [==============================] - 134s 231ms/step - loss: 0.4208 - accuracy: 0.9484 - val_loss: 0.1907 - val_accuracy: 0.9812\n",
            "Epoch 12/20\n",
            "581/581 [==============================] - 114s 197ms/step - loss: 0.3359 - accuracy: 0.9570 - val_loss: 0.1835 - val_accuracy: 0.9844\n",
            "Epoch 13/20\n",
            "581/581 [==============================] - 116s 200ms/step - loss: 0.2930 - accuracy: 0.9650 - val_loss: 0.1505 - val_accuracy: 0.9844\n",
            "Epoch 14/20\n",
            "581/581 [==============================] - 114s 196ms/step - loss: 0.2561 - accuracy: 0.9701 - val_loss: 0.1575 - val_accuracy: 0.9859\n",
            "Epoch 15/20\n",
            "581/581 [==============================] - 119s 206ms/step - loss: 0.2302 - accuracy: 0.9715 - val_loss: 0.1600 - val_accuracy: 0.9812\n",
            "Epoch 16/20\n",
            "581/581 [==============================] - 115s 197ms/step - loss: 0.2134 - accuracy: 0.9747 - val_loss: 0.1407 - val_accuracy: 0.9828\n",
            "Epoch 17/20\n",
            "581/581 [==============================] - 115s 197ms/step - loss: 0.1546 - accuracy: 0.9813 - val_loss: 0.0944 - val_accuracy: 0.9828\n",
            "Epoch 18/20\n",
            "581/581 [==============================] - 116s 200ms/step - loss: 0.1636 - accuracy: 0.9794 - val_loss: 0.0947 - val_accuracy: 0.9844\n",
            "Epoch 19/20\n",
            "581/581 [==============================] - 115s 198ms/step - loss: 0.1356 - accuracy: 0.9823 - val_loss: 0.1169 - val_accuracy: 0.9828\n",
            "Epoch 20/20\n",
            "581/581 [==============================] - 116s 199ms/step - loss: 0.1243 - accuracy: 0.9849 - val_loss: 0.1121 - val_accuracy: 0.9875\n"
          ]
        }
      ],
      "source": [
        "fine_tune_epochs = 10\n",
        "total_epochs =  initial_epochs + fine_tune_epochs\n",
        "\n",
        "history_fine = model.fit(train_batches.repeat(), \n",
        "                         steps_per_epoch = steps_per_epoch,\n",
        "                         epochs=total_epochs, \n",
        "                         initial_epoch = initial_epochs,\n",
        "                         validation_data=validation_batches.repeat(), \n",
        "                         validation_steps=validation_steps)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "TfXEmsxQf6eP"
      },
      "source": [
        "### Learning curves\n",
        "\n",
        "Take a look at the learning curves of the training and validation accuracy / loss, after fine-tuning. \n",
        "\n",
        "Note: the training dataset is fairly small, and is similar to the original datasets that MobileNet V2 was trained on, so fine-tuning may result in overfitting.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "DNtfNZKlInGT"
      },
      "source": [
        "After fine tuning the model nearly reaches 98% accuracy."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "PpA8PlpQKygw"
      },
      "outputs": [],
      "source": [
        "acc += history_fine.history['accuracy']\n",
        "val_acc += history_fine.history['val_accuracy']\n",
        "\n",
        "loss += history_fine.history['loss']\n",
        "val_loss += history_fine.history['val_loss']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 512
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 2289162,
          "status": "ok",
          "timestamp": 1551653793333
        },
        "id": "chW103JUItdk",
        "outputId": "c681fe0e-a256-48e1-e9e1-8e6e4667e945"
      },
      "outputs": [
        {
          "data": {
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dwiFbKUV1JRysL6Go/hA1TVbf6ypUxIWZ6R+eQr/wZPqF9yXJ2KfbyidJWwhxQbC32Kl0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\n",
            "text/plain": [
              "\u003cFigure size 576x576 with 2 Axes\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(8, 8))\n",
        "plt.subplot(2, 1, 1)\n",
        "plt.plot(acc, label='Training Accuracy')\n",
        "plt.plot(val_acc, label='Validation Accuracy')\n",
        "plt.ylim([0.8, 1])\n",
        "plt.plot([initial_epochs-1,initial_epochs-1], \n",
        "          plt.ylim(), label='Start Fine Tuning')\n",
        "plt.legend(loc='lower right')\n",
        "plt.title('Training and Validation Accuracy')\n",
        "\n",
        "plt.subplot(2, 1, 2)\n",
        "plt.plot(loss, label='Training Loss')\n",
        "plt.plot(val_loss, label='Validation Loss')\n",
        "plt.ylim([0, 1.0])\n",
        "plt.plot([initial_epochs-1,initial_epochs-1], \n",
        "         plt.ylim(), label='Start Fine Tuning')\n",
        "plt.legend(loc='upper right')\n",
        "plt.title('Training and Validation Loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "_TZTwG7nhm0C"
      },
      "source": [
        "# Key takeaways\n",
        "In summary here is what we covered in this tutorial on how to do transfer learning using a pre-trained model to improve accuracy:\n",
        "* Using a pre-trained model for **feature extraction** - when working with a small dataset, it is common to leverage the features learned by a model trained on a larger dataset in the same domain. This is done by instantiating the pre-trained model and adding a fully connected classifier on top. The pre-trained model is \"frozen\" and only the weights of the classifier are updated during training.\n",
        "In this case, the convolutional base extracts all the features associated with each image and we train a classifier that determines, given these set of features to which class it belongs.\n",
        "* **Fine-tuning** a pre-trained model - to further improve performance, one might want to repurpose the top-level layers of the pre-trained models to the new dataset via fine-tuning.\n",
        "In this case, we tune our weights such that we learn highly specified and high level features specific to our dataset. This only make sense when the training dataset is large and very similar to the orginial dataset that the pre-trained model was trained on.\n"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "last_runtime": {
        "build_target": "//learning/brain/python/client:colab_notebook",
        "kind": "private"
      },
      "name": "transfer_learning.ipynb",
      "provenance": [],
      "toc_visible": true,
      "version": "0.3.2"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
